# Deep Convolutional Autoencoder Github

 It is a basic reduction operation. Graph clustering aims to discover community structures in networks, the task being fundamentally challenging mainly because the topology structure and the content of the graphs are dicult to represent for clustering analysis. To make things worse deconvolutions do exists, but they’re not common in the field of deep learning. Outlier Detection Using Replicator Neural Networks 2002 pdf. Contents 1: Machine Learning Review b'Chapter 1: Machine Learning Review' b'Machine learning \xe2\x80\x93 history and definition' b'What is not machine learning?' b'Machine learning \xe2\x80\x93 concepts and terminology' b'Machine learning \xe2\x80\x93 types and subtypes' b'Datasets used in machine learning' b'Machine learning applications' b'Practical issues in machine learning' b'Machine. When we train an Autoencoder, we’ll actually be training an Artificial Neural Network that. Extensive number of deep learning methods (LeCun et al. In Understanding Generative. These models are typically trained by taking high resolution images and reducing them to lower resolution and then train in the opposite way. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. Hosseini-Asl, "Structured Sparse Convolutional Autoencoder", arXiv:1604. Deconvolution side is also known as unsampling or transpose convolution. YAPAY SİNİR AĞLARI VE MAKİNE ÖĞRENMESİ Udacity Makine Öğrenmesi Kursu BLOGLAR: DERİN ÖĞRENME: Udacity Derin Öğr…. Shirui Pan is a Lecturer (a. eager_dcgan: Generating digits with generative adversarial networks and eager execution. Dense autoencoder: compressing data. Abnormal Event Detection in Videos using Spatiotemporal Autoencoder pdf. Geometric Deep Learning. recently, collaborative deep learning (CDL) [29] and collaborative recurrent autoencoder [30] have been proposed for joint learning a stacked denoising autoencoder (SDAE) [26] (or denoising recurrent autoencoder) and collaborative •ltering, and they shows promising performance. This helps the network extract visual features from the images, and therefore obtain a much more accurate latent. Applies some math to it (I won’t get into the specifics of Deep Learning right now, but this is the book I used to learn these subjects). Further, the neurons in one layer do not connect to all the neurons in the next layer but only to a small region of it. this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between the network structure and node attribute for high-quality embeddings. of Brain and Cognitive Sciences at University of Rochester and a member of Computational Cognition and Perception lab. The encoder is a neural network. of Deep Embedded Clustering with Data Augmentation (DEC-DA). Robust, Deep and Inductive Anomaly Detection Raghavendra Chalapathy1, Aditya Krishna Menon2, and Sanjay Chawla3 1 University of Sydney and Capital Markets Cooperative Research Centre (CMCRC) 2 Data61/CSIRO and the Australian National University 3 Qatar Computing Research Institute [email protected] Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature, partly as a response the common criticism that the learned features in a Neural Network are not interpretable. I am trying to make a simple Convolutional Autoencoder with weights tied in Lasagne This is the main part which create the model in Lasage, the other part is just training it on MNIST data. Other resources. A careful reader could argue that the convolution reduces the output’s spatial extent and therefore is not possible to use a convolution to reconstruct a volume with the same spatial extent of its input. Additionally, in almost all contexts where the term "autoencoder" is used, the compression and decompression functions are implemented with neural networks. There are many ways to do content-aware fill, image completion, and inpainting. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. In addition, even after detecting nodule candidates, a considerable amount of effort and time is required for them to determine whether. CoRR abs/1802. Recommender - Wide & Deep Network. CNN as you can now see is composed of various convolutional and pooling layers. Datasets: Neural Message Passing for Quantum Chemistry. Convolutional autoencoders - Deep Learning with TensorFlow. CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. KDD'18 Deep Learning Day, August 2018, London, UK R. We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well. You should study this code rather than merely run it. the Hourglass CNN layers [22] are used to learn the convolutional features, then the feature maps for different joints are fed to the LSTD module with CCG for feature boosting. Run Keras models in the browser, with GPU support provided by WebGL 2. Repo for the Deep Learning Nanodegree Foundations program. In practical settings, autoencoders applied to images are always convolutional autoencoders — they simply perform much better. The following is the old post: Dear Viewers, I'm sharing a lecture note of " Deep Learning Tutorial - From Perceptrons to Deep Networks ". However, Karpathy is not actively maintaining ConvNetJS anymore because he don't have time. These, along with pooling layers, convert the input from wide and thin (let's say 100 x 100 px with 3 channels — RGB) to narrow and thick. A collection of various deep learning architectures, models, and tips. Deep-Convolutional-AutoEncoder. Ruta is based in the well known open source deep learning library Keras and its R interface. Code: Keras. This is also an example for using the deconvolutional layer or the transposed fractional stride convolutional layers. gl/bdMDVG 1. Kulkarni*1, Will Whitney*2, Pushmeet Kohli3, Joshua B. It is written in C++, with a Python interface. The goal of the tutorial is to provide a simple template for convolutional autoencoders. A stacked denoising autoencoder Output from the layer below is fed to the current layer and training is done layer wise. Convolutional Neural Networks Convolutional Neural Networks (CNNs) have proven great capability of learning important features from images at the pixel level in order to make. Welling Users Items 0 0 2 0 0 0 0 4 5 0 0 1 0 3 0 0 5 0 0 0 rs Items Rating matrix. Identity Mappings in Deep Residual Networks (published March 2016). Deep autoencoder 11. T2R is a library for training, evaluation and inference of large-scale deep neural networks. It is not an autoencoder variant, but rather a traditional autoencoder stacked with convolution layers: you basically replace fully connected layers by convolutional layers. Christian Theobalt 7,845 views. I was surprised with the results: compressing the image to a fourth of its size with the cat still being recognizable, means an image classifier (like a Convolutional Neural Network) could probably tell there was a cat in the picture. Awesome Deep Learning @ July2017. 9 patches with strongest. 1) Specific details. Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. Deep Learning. NumPy; Tensorflow; Keras; OpenCV; Dataset. It consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. Convolutional AutoEncoder application on MRI images. The project code can be found in this repository. A related paper, Deep Convolutional Generative Adversarial Networks, and the available source code. The code for each type of autoencoder is available on my GitHub. My concentration is in: 1) Algorithms- architecture and development. Geometric Deep Learning. Oliva, and A. All models have as close as possible nets architectures and implementations with necessary deviations required by their articles. Furthermore, deep spectral clustering is harnessed to embed the latent repre-sentations into the eigenspace, which followed by cluster-ing. Geometric Deep Learning. Reconstructing original images based on CNN Codes. Deep learning¶ "Deep" neural networks typically refer to networks with multiple hidden layers. Speciﬁcally, we ﬁrst train an autoencoder with the augmented data to construct the initial feature space. 2 Convolutional Winner-Take-All autoencoder. Deep Convolutional Neural Network for Plant Seedlings Classification. Why GitHub? Features → Code review Convolutional_Autoencoder. We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder. This gives the model 32, or even 512, different ways of extracting features from an input, or many different ways of both “ learning to see ” and after training, many different ways of “ seeing ”. Anomaly Detection with Robust Deep Auto-encoders KDD 2017 pdf. Want to jump right into it? Look into the notebooks. The deep learning approaches for network embedding at the same time belong to graph neural networks, which include graph autoencoder-based algorithms (e. Thanks for reading this. the convolution autoencoder network. ipynb: stride reduces the size by a factor: Jul 20, 2017: Convolutional_Autoencoder_Solution. Geometric Deep Learning. They work by compressing the input into a latent-spacerepresentation, and then reconstructing the output from this representation. We will use the ped1 part for training and testing. GoogLeNet is a pretrained convolutional neural network that is 22 layers deep. Conditional Variational Autoencoder (CVAE) is an extension of Variational Autoencoder (VAE), a generative model that we have studied in the last post. testing_repo specifies the location of the test data. 3D-Generative Adversial Network. developed AtomNet, a deep convolutional neural network (CNN), for modeling bioactivity and chemical interactions (Wallach et al. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG. Assistant Professor) with the Machine Learning Group, Faculty of Information Technology, Monash University. Chainer Implementation of Convolutional Variational AutoEncoder - cvae_net. Lasagne is a high-level interface for Theano. In order to illustrate the different types of autoencoder, an example of each has been created, using the Keras framework and the MNIST dataset. The discriminator is run using the output of the autoencoder. Moreover, the capsule network is proposed to solve problems of current convolutional neural network and achieves state-of-the-art performance on MNIST data set. Datasets: Neural Message Passing for Quantum Chemistry. Deep Learning Applications. paper: http://arxiv. Introduction. El-Baz, “Multimodel Alzheimer’s Disease Diagnosis by Deep Convolutional CCA”, in preparation for submission to Medical Imaging, IEEE Transactions on. Restricted Boltzmann Machine (RBM) Sparse Coding. Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics 3 approach, however, is efficient for very large models as splitting a neural network model needs to be done in a case-by-case manner and is very time-consuming. This paper presents the development of several models of a deep convolutional auto-encoder in the Caffe deep learning framework and their experimental evaluation on the example of MNIST dataset. In Understanding Generative. [pdf | code] Xifeng Guo, Xinwang Liu, En Zhu, Xinzhong Zhu, Miaomiao Li, Xin Xu, and Jianping Yin. Stronger variant of denoising autoencoders. This paper contributes a new type of model-based deep convolutional autoencoder that joins forces of state-of-the-art generative and CNN-based regression approaches for dense 3D face reconstruction via a deep integration of the two on an architectural level. Published on Jul 29, 2016. a neural net with one hidden layer. We have created five models of a convolutional auto-encoder which differ architecturally by the presence or absence of pooling and unpooling layers in the auto-encoder's encoder and decoder parts. Also, I value the use of tensorboard, and I hate it when the resulted graph and parameters of the model are not presented clearly in the tensorboard. - udacity/deep-learning. intro: “built action models from shape and motion cues. A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. 2D-and-3D-Deep-Autoencoder. Subscription. Autoencoder. The main difficulty is that lesions can be anywhere, have any shape and any size. A Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks Quoc V. Before this, Go was considered to be an intractable game for computers to master, as its simple rules. 3) Pattern recognition, NLP, Computer Vision and medical image analysis. This article is an export of the notebook Deep feature consistent variational auto-encoder which is part of the bayesian-machine-learning repo on Github. 2) Convolutional autoencoder. Prior to this, he was a Lecturer with the Centre for Artificial Intelligence (CAI) , School of Software, Faculty of Engineering and Information Technology , University of Technology Sydney(UTS). Convolutional Autoencoder. Deep Learning models are build by stacking an often large number of neural network layers that perform feature engineering steps, e. 6 Filters and Basis Functions obtained. Suppose further this was done with an autoencoder that has 100 hidden units. In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). Welcome to Python Machine Learning course!¶ Table of Content. Repo for the Deep Learning Nanodegree Foundations program. The convolution operator allows filtering an input signal in order to extract some part of its content. We pass an input image to the first convolutional layer. We present an efficient method for detecting anomalies in videos. The code is written using the Keras Sequential API with a tf. Two models are trained simultaneously by an. Learned convolutional filters: Stage 1. Organizing the SocialNLP workshop in ACL 2018 and WWW 2018 is four-fold. They start from the image proposals and select the motion salient subset of them and extract saptio-temporal features to represent the video using the CNNs. We construct 4 convolutional layers in the encoder network with 4 × 4 kernel and 2 ×. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. The network trained on Places365 is similar. The autoencoder can be made of densely connected neurons and also with convolutional neural networks. [ 12 ] proposed image denoising using convolutional neural networks. Convolutional ANNs. Deep learning is a division of machine learning and is cons. Zeiler, Matthew D. conv2d_transpose(). A related paper, Deep Convolutional Generative Adversarial Networks, and the available source code. a neural net with one hidden layer. Thanks to deep learning, computer vision is working far better than just two years ago,. So, we’ve integrated both convolutional neural networks and autoencoder ideas for information reduction from image based data. the convolution autoencoder network. Course: Deep Learning. This project was done for the final year engineering project, as a part of curriculum. You can load a network trained on either the ImageNet [1] or Places365 [2] [3] data sets. Repo for the Deep Learning Nanodegree Foundations program. GradientTape training loop. However, the cost obtained by crossentropy is extreme small and the reconstructed images is just full of black. A utoencoders (AE) are neural networks that aims to copy their inputs to their outputs. Θ- regression (convolutional) module; generates dense flow map H - Huber penalty: motion locally smooth 10 Temporal decoder: less parameters than the encoder Spatio-temporal video autoencoder (ICLRw2016). Reconstructing original images based on CNN Codes. However, in these deep learning models, drugs are represented as was not certified by peer review) is the author/funder. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. This network takes as input 100 random numbers drawn from a uniform distribution and outputs an image of desired shape. Welling Users Items 0 0 2 0 0 0 0 4 5 0 0 1 0 3 0 0 5 0 0 0 rs Items Rating matrix. routine [12], led to substantial success in many deep learning tasks. Although a simple concept, these representations, called codings, can be used for a variety of dimension reduction needs, along with additional uses such as anomaly detection and generative modeling. Create an Auto-Encoder using Keras functional API: Autoencoder is a type a neural network widely used for unsupervised dimension reduction. Geometric Deep Learning. Convolutional Neural Networks for Particle Tracking Steve Farrell for the HEP. Datasets: Neural Message Passing for Quantum Chemistry. Autoencoder - unsupervised embeddings, denoising, etc. In Understanding Generative. Deep multi-scale video prediction pdf, ST-video autoencoder with differentiable memory pdf , Slides , CortexNet: Robust Visual Temporal Representations pdf. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla, Senior Member, IEEE, Abstract—We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This course provides an introduction to the Neural Network Model and deep learning methodologies applied to NLP from scratch. With the rapid growth and high prevalence of Internet services and applications, people have access to large amounts of online multi- media content, such as movies, music, news and articles. Code: Keras. The H2O Deep Learning in R Tutorial that provides more background on Deep Learning in H2O, including how to use an autoencoder for unsupervised pretraining. Simple Image Classification using Convolutional Neural Network — Deep Learning in python. Convolutional Autoencoder with Transposed Convolutions. Solution: use a convolutional autoencoder which incorporate a “segmentation layer” before the output. The input images for the convolutional neural network and the AlexNet neural network have the 100×100 dimensions with colors, and the input images for the unsupervised Autoencoder neural network have the dimension of 50×50×3, which would greatly reduce the computational load for this model. Xception and the Depthwise Separable Convolutions: Xception is a deep convolutional neural network architecture that involves Depthwise Separable Convolutions. , 2015; Pan et al. 6] : Deep learning - deep autoencoder Deep Learning for Speech Lex Fridman 53,754 views. Die Papiere sind nicht nur nach Sternen sortiert, sondern auch nach Jahr geordnet, was es. This procedure can exploit the relationships between the data points effectively and obtain the optimal results. The general autoencoder architecture example - Unsupervised Feature Learning and Deep Learning Tutorial. , DNGR [41] and SDNE [42]) and graph convolution neural networks with unsupervised training(e. The deep learning approaches for network embedding at the same time belong to graph neural networks, which include graph autoencoder-based algorithms (e. , autoencoder, suggesting that. Getting Dirty With Data. In addition to. A deep convolutional denoising autoencoder method based on total variational multi-norm loss function minimization approach has been introduced for the restoration of mammograms. 04/27/20 - Benefited from the deep learning, image Super-Resolution has been one of the most developing research fields in computer vision. A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. It’s easy to create well-maintained, Markdown or rich text documentation alongside your code. Deep Convolutional Inverse Graphics Network Tejas D. In November 2015, Google released TensorFlow (TF), "an open source software library for numerical computation using data flow graphs". Given a training dataset of corrupted data as input and true signal as output, a denoising autoencoder can recover the hidden structure to generate clean data. This article is about summary and tips on Keras. [ 12 ] proposed image denoising using convolutional neural networks. So, we’ve integrated both convolutional neural networks and autoencoder ideas for information reduction from image based data. g embedding, and are collapsed in a final softmax layer (basically a logistic regression layer). The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. It is trained for next-frame video prediction with the belief that prediction is an effective objective for unsupervised (or "self-supervised") learning [e. Deep multi-scale video prediction pdf, ST-video autoencoder with differentiable memory pdf , Slides , CortexNet: Robust Visual Temporal Representations pdf. Developed a web-based desktop application to deploy the model using Python and Flask. The decoder reconstructs the data given the hidden representation. However, the cost obtained by crossentropy is extreme small and the reconstructed images is just full of black. In our VAE example, we use two small ConvNets for the generative. The Convolutional Winner-Take-All Autoencoder (Conv-WTA) [16] is a non-symmetric au-toencoder that learns hierarchical sparse representations in an unsupervised fashion. Autoencoder Applications: Diachronic Linguistics to Identify Emerging Threats To achieve this goal, we create a novel deep learning framework, the Diachronic Graph Convolutional Autoencoder (D-GCAE). Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning. intro: CVPR 2017 Ensemble Deep Learning. deep convolutional autoencoder network for assessing the VR sickness of VR video content. Codes with only numpy. Deep Learning models are build by stacking an often large number of neural network layers that perform feature engineering steps, e. Convolutional Autoencoder 今度は畳み込みニューラルネットワーク（convolutional neural network, CNN）を使うことを考えます。 一般に、主に画像認識においてCNNは普通のニューラルネットワーク（multilayer perceptron, MLP）よりもパフォーマンスが高いことが知られています。. The main difficulty is that lesions can be anywhere, have any shape and any size. They take images as inputs, and output a probability distribution of the classes. Recent advance in deep. I am trying to make a simple Convolutional Autoencoder with weights tied in Lasagne This is the main part which create the model in Lasage, the other part is just training it on MNIST data. Datasets: Neural Message Passing for Quantum Chemistry. The model achieves 92. Github: Autoencoder network for learning a continuous representation of molecular structures. Tensorflow Auto-Encoder Implementation. What this means is our encoding and decoding models will be convolutional neural networks instead of fully-connected networks. Lasagne is a high-level interface for Theano. Hosseini-Asl, “Structured Sparse Convolutional Autoencoder”, arXiv:1604. Real-world image super-resolution is a challenging image translation problem. How was the advent and evolution of machine learning?. Dai 40 [Badrinarayanan et al. 3dgan_feature_matching. 3dgan_autoencoder. Autoencoder Applications: Diachronic Linguistics to Identify Emerging Threats To achieve this goal, we create a novel deep learning framework, the Diachronic Graph Convolutional Autoencoder (D-GCAE). Recently, graph clustering has moved from traditional shallow methods to deep learning approaches, thanks to the unique feature representation learning capability of deep. En büyük profesyonel topluluk olan LinkedIn‘de Ahmet Melek adlı kullanıcının profilini görüntüleyin. This tutorial was designed for easily diving into TensorFlow, through examples. Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. It consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. VGG16 is a convolutional neural network model proposed by K. Bringing in code from IndicoDataSolutions and Alec Radford (NewMu) Additionally converted to use default conv2d interface instead of explicit cuDNN. Define autoencoder model architecture and reconstruction loss Using $28 \times 28$ image, and a 30-dimensional hidden layer. Concrete autoencoder A concrete autoencoder is an autoencoder designed to handle discrete features. 実装と簡単な補足は以下. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Repo for the Deep Learning Nanodegree Foundations program. Are you sure you want to Yes No. handong1587's blog. Striving for Simplicity: The All Convolutional Net. Autoencoders in general are used to learn a representation, or encoding, for a set of unlabeled data, usually as the first step towards dimensionality reduction or generating new data models. We will use the UCSD anomaly detection dataset, which contains videos acquired with a camera mounted at an elevation, overlooking a pedestrian walkway. In the encoder, the input data passes through 12 convolutional layers with 3x3 kernels and filter sizes starting from 4 and increasing up to 16. A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. It is com- posed of a convolutional autoencoders and a clustering layer connected to embedded layer of autoencoders. Deep Learning Knowledge Discovery and Data Mining 2 (VU) (706. Deep Learning models are build by stacking an often large number of neural network layers that perform feature engineering steps, e. This repository provides a python-based toolbox called deephyp, with examples for building, training and testing both dense and convolutional autoencoders and classification neural networks, designed for hyperspectral data. - udacity/deep-learning. Autoencoders in their traditional formulation do not take into account the fact that a signal can be seen as a sum of other signals. Autoencoders — Deep Learning bits #1. Download the UCSD dataset and extract it into your current working directory or create a new notebook in Kaggle using this dataset. This gives the model 32, or even 512, different ways of extracting features from an input, or many different ways of both “ learning to see ” and after training, many different ways of “ seeing ”. Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. 6] : Deep learning - deep autoencoder Deep Learning for Speech Lex Fridman 53,754 views. It was shown that denoising autoencoders can be stacked to form a deep network by feeding the output of one denoising autoencoder to the one below it. The process of choosing the important parts of data is known as feature selection, which is among the number of use cases of an autoencoder. Another implementation of an adversarial autoencoder. This tutorial was designed for easily diving into TensorFlow, through examples. For example, we can use neural networks to estimate the 3D geometry of an object from a single input view. We have created five models of a convolutional auto-encoder which differ architecturally by the presence or absence of pooling and unpooling layers in the auto-encoder's encoder and decoder parts. and first applied to biological data by Bengio et al. In its simplest form, the autoencoder is a three layers net, i. Ruta is based in the well known open source deep learning library Keras and its R interface. A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. com The encoder consists of three convolutional layers. Therefore, many less-important features will be ignored by the encoder (in other words, the decoder can only get. (just to name a few). Vincent et al. Advanced and experimental deep learning features might reside within. We convert the image matrix to an array, rescale it between 0 and 1, reshape it so that it’s of size 224 x 224 x 1, and feed this as an input to the network. Building Sparse Deep Feedforward Networks using Tree Receptive Fields. •Decoder: Upsampling + convolutional filters •The convolutional filters in the decoder are learned using backprop and their goal is to refine the upsampling I2DL: Prof. Its input is a datapoint. At the time of writing the last blog post, I was awaiting further results from an expanded DCGAN with additional. Save / Load NetworkData ( weights). The cluster-. It has been developed to work with the TensorFlow backend. Convolutional ANNs. Introduction. Between Scylla and Charybdis: Convolutional Autoencoder. An actual deconvolution reverts the process of a convolution. Document your code. CNN :These stand for convolutional neural. On the other hand, Variational autoencoder has attracted much attention recently because of its ability to do efﬁcient inference. In normal settings, these videos contain only pedestrians. Tests on multiple datasets show better results than the original CAE model along with other contemporary approaches. The Convolutional Winner-Take-All Autoencoder (Conv-WTA) [16] is a non-symmetric au-toencoder that learns hierarchical sparse representations in an unsupervised fashion. Jan 4, 2016 ####NOTE: It is assumed below that are you are familiar with the basics of TensorFlow! Introduction. au,[email protected] We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well. Conclusions. Naturally, the data and filters will be 3D entities that can be imagined as a volume rather than a flat image or matrix. A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. 2D-and-3D-Deep-Autoencoder. VAE blog; VAE blog; I have written a blog post on simple autoencoder here. The suggested model can extract relevant features and can reduce the dimensionality of the image data while preserving the key features that have been applied to. This forces the smaller hidden encoding layer to use dimensional reduction to eliminate noise and reconstruct the inputs. Convolutional Autoencoder. The deep neural network used a single hologram intensity as input, whereas N holo =8 was used in the column on the right. eager_image. VAE is considered as a powerful method in unsupervised learning, which is highly expressive with its stochastic variables. 2 Convolutional Winner-Take-All autoencoder. According to the CS 231 n page on Convolutional Networks, there are only two values for the kernel size that are usually used - 2 and 3, and the stride is usually just 2, with a kernel size of 2 being more common, and as it turns out, a kernel size of 2 and a stride of 2 will reduce our input dimensions by a half, which is what we want. A novel variational autoencoder is developed to model images, as well as associated labels or captions. Thanks to deep learning, computer vision is working far better than just two years ago,. This tutorial was designed for easily diving into TensorFlow, through examples. Denoising autoencoder: removing noise from poor training data. distinguished as pore space (Phi), quartz (Qtz), K-feldspar (K-Fld), zircon (Zrc) and other minerals (i. Middle: The data is zero-centered by subtracting the mean in each dimension. Save / Load NetworkData ( weights). Thanks to deep learning, computer vision is working far better than just two years ago,. Effective way to load and pre-process data, see tutorial_tfrecord*. intro: CVPR 2017 Ensemble Deep Learning. gl/bdMDVG 1. Tensorflow's Keras API is a lot more comfortable and. org/rec/journals/corr/abs-1802-00003 URL. activation uses relu non-linearities. In theory, DBNs should be the best models but it is very hard to estimate joint probabilities accurately at the moment. Published: Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification //dashee87. As before, we start from the bottom with the input $\boldsymbol{x}$ which is subjected to an encoder (affine transformation defined by $\boldsymbol{W_h}$, followed by squashing). Deep Learning-powered image recognition is now performing better than human vision on many tasks. Convolutional autoencoder We may also ask ourselves: can autoencoders be used with Convolutions instead of Fully-connected layers ? The answer is yes and the principle is the same, but using images (3D vectors) instead of flattened 1D vectors. The following is the old post: Dear Viewers, I'm sharing a lecture note of " Deep Learning Tutorial - From Perceptrons to Deep Networks ". Codes with only numpy. Autoencoders One way to do unsupervised training in a ConvNet is to create an autoencoder architecture with convolutional. • Deﬁnition 5: “Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artiﬁcial. Convolutional Layer: Weights Sharing, Local Connectivity It is impractical to connectneurons to all neurons in the previous volume. A similar post describing generative adversarial autoencoders. Right: Each dimension is additionally scaled by its standard deviation. edu [email protected] A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. CMYK Cyan-Magenta-Yellow-blacK 26. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. Um Deep Learning besser und schneller lernen, es ist sehr hilfreich eine Arbeit reproduzieren zu können. Deep Convolutional Neural Network for Plant Seedlings Classification. If our inputs are images, it makes sense to use convolutional neural networks (convnets) as encoders and decoders. Denoising autoencoder, some inputs are set to missing Denoising autoencoders can be stacked to create a deep network (stacked denoising autoencoder) [25] shown in Fig. A careful reader could argue that the convolution reduces the output’s spatial extent and therefore is not possible to use a convolution to reconstruct a volume with the same spatial extent of its input. Created a Deep Learning Application for an Insurance firm to predict the future costs of the firm and the most probable future disease for its customers. Convolutional Autoencoders, instead, use the convolution operator to exploit this observation. py: data input output and plotting utilities. arXiv:1805. 3dgan as mentioned in the paper with bias in convolutional layers. learned filters (7x7x96) These visualizations by Matt Zeiler and Rob Fergus will give you an idea of what the network is doing at. In its simplest form, the autoencoder is a three layers net, i. The models are: Deep Convolutional GAN, Least Squares GAN, Wasserstein GAN, Wasserstein GAN Gradient Penalty, Information Maximizing GAN, Boundary Equilibrium GAN, Variational AutoEncoder and Variational AutoEncoder GAN. GraphConv and dgl. Striving for Simplicity: The All Convolutional Net. combined the Deep Auto-Encoder with Hidden Markov Model to investigate the functional connectivity in resting-state fMRI; Huang et al. Naturally, the data and filters will be 3D entities that can be imagined as a volume rather than a flat image or matrix. intro: “built action models from shape and motion cues. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. It is written in C++, with a Python interface. Xception and the Depthwise Separable Convolutions: Xception is a deep convolutional neural network architecture that involves Depthwise Separable Convolutions. We present a novel method for constructing Variational Autoencoder (VAE). En büyük profesyonel topluluk olan LinkedIn‘de Ahmet Melek adlı kullanıcının profilini görüntüleyin. I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol in 2016. A similar post describing generative adversarial autoencoders. The code is written using the Keras Sequential API with a tf. NE], (code-python/theano) E. 13: Architecture of a basic autoencoder. See this TF tutorial on DCGANs for an example. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla, Senior Member, IEEE, Abstract—We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. My last post on variational autoencoders showed a simple example on the MNIST dataset but because it was so simple I thought I might have missed some of the subtler points of VAEs -- boy was I right! The fact that I'm not really a computer vision guy nor. Tensorflow Auto-Encoder Implementation. Autoencoder Foundations of Data Analysis April 28, 2020 1 Slide credits to Jian Wang, PhD student, CS, UVA. Common data preprocessing pipeline. Galeone's blog Autoencoder: Downsampling and Upsampling - GitHub Pages. Deep autoencoder 11. Lasagne is a high-level interface for Theano. Yet, until recently, very little attention has been devoted to the generalization of neural. We believe that our approach and results presented in this paper could help other researchers to build efficient deep neural network architectures in the future. The same filters are slid over the entire image to find the relevant features. Total stars 2,710 Stars per day 3 Created at 2 years ago Related Repositories face2face-demo pix2pix demo that learns from facial landmarks and translates this into a face pytorch-made MADE (Masked Autoencoder Density Estimation) implementation in PyTorch. They take images as inputs, and output a probability distribution of the classes. Different from [14], which employs one single graph convolutional network (GCN) [15] based. Example convolutional autoencoder implementation using PyTorch - example_autoencoder. , 2016; Pan and Shen, 2017; Zhang et al. Chainer Implementation of Convolutional Variational AutoEncoder - cvae_net. Affine Equivariant Autoencoder. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation. Real-world image super-resolution is a challenging image translation problem. The models are: Deep Convolutional GAN, Least Squares GAN, Wasserstein GAN, Wasserstein GAN Gradient Penalty, Information Maximizing GAN, Boundary Equilibrium GAN, Variational AutoEncoder and Variational AutoEncoder GAN. This network takes as input 100 random numbers drawn from a uniform distribution and outputs an image of desired shape. 6 Filters and Basis Functions obtained. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. Semantic Autoencoder for Zero-Shot Learning. activation. 2 Convolutional Winner-Take-All autoencoder. of Brain and Cognitive Sciences at University of Rochester and a member of Computational Cognition and Perception lab. GoogLeNet is a pretrained convolutional neural network that is 22 layers deep. Deep Learning Applications. 1109/ICASSP. LinkedIn‘deki tam profili ve Ahmet Melek adlı kullanıcının bağlantılarını ve benzer şirketlerdeki işleri görün. Autoencoders — Deep Learning bits #1. Application of a deep convolutional autoencoder network on MRI images of knees. (a–f) Blood smear. Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. The full source code is on my GitHub, read until the end of the notebook since you will discover another alternative way to minimize clustering and autoencoder loss at the same time which proven to be useful to improve the clustering accuracy of the convolutional clustering model. Shirui Pan is a Lecturer (a. 04/27/20 - Benefited from the deep learning, image Super-Resolution has been one of the most developing research fields in computer vision. “Learning Deep Features for Discriminative Localization” proposed a method to enable the convolutional neural network to have localization ability despite being trained on image-level labels. The autoencoder is a neural network that learns to encode and decode automatically (hence, the name). autoencoder. To address this issue, this paper proposes a novel Style-based Super-Resolution Variational Autoencoder network (SSRVAE) that contains a style Variational Autoencoder. Between Scylla and Charybdis: Convolutional Autoencoder Posted on April 30, 2017 May 5, 2017 by wperrault The last post detailed how I started out with the training of a deep convolutional GAN, which proved to be challenging. Convolutional Autoencoder 今度は畳み込みニューラルネットワーク（convolutional neural network, CNN）を使うことを考えます。 一般に、主に画像認識においてCNNは普通のニューラルネットワーク（multilayer perceptron, MLP）よりもパフォーマンスが高いことが知られています。. Towards Data Science. cv-foundation. Imagine inputting an image into a single convolutional layer. edu [email protected] Middle: The data is zero-centered by subtracting the mean in each dimension. A related paper, Deep Convolutional Generative Adversarial Networks, and the available source code. That would be pre-processing step for clustering. Though semi. Your message goes here. Denoising autoencoder¶ learn a more robust representation by forcing the autoencoder to learn an input from a corrupted version of itself; Autoencoders and inpainting. No prizes for guessing the deep learning framework on which Tensor2Robot is built. This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). In Proceedings of International Joint Conference on Artiﬁcial Intelligence, Jul. The following is the old post: Dear Viewers, I'm sharing a lecture note of " Deep Learning Tutorial - From Perceptrons to Deep Networks ". In this article we will train a convolutional neural network to classify clothes types from the fashion MNIST dataset. [ 12 ] proposed image denoising using convolutional neural networks. Autoencoders (AE) are a family of neural networks for which the input is the same as. Conceptually, both of the models try to learn a rep-. In addition to. DenseNet-121, trained on ImageNet. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating. conv_lstm: Demonstrates the use of a convolutional LSTM network. Suppose further this was done with an autoencoder that has 100 hidden units. deep_dream: Deep Dreams in Keras. 2 Convolutional Winner-Take-All autoencoder. We propose a spatiotemporal architecture for anomaly detection in videos. conv2d_transpose(). We convert the image matrix to an array, rescale it between 0 and 1, reshape it so that it's of size 224 x 224 x 1, and feed this as an input to the network. FCN Fully Convolutional. Convolutional autoencoder. Autoencoder is an artificial neural network used for unsupervised learning of efficient codings. One of the major goals in this area is to reconstruct 3D content from observations. Example convolutional autoencoder implementation using PyTorch - example_autoencoder. Linear convolutional decoder. readthedocs. Then we constrain the embedded features with a clustering loss to further learn clustering-oriented features. Want to jump right into it? Look into the notebooks. Single model without data augmentation. Y Yang, Q Fang, HB Shen, Predicting gene regulatory interactions based on spatial gene expression data and deep learning, PLOS Computational Biology, 2019 (in press). Naturally, the data and filters will be 3D entities that can be imagined as a volume rather than a flat image or matrix. A collection of various deep learning architectures, models, and tips. Vanilla autoencoder. The code for each type of autoencoder is available on my GitHub. Autoencoders can encode an input image to a latent vector and decode it, but they can’t generate novel images. Chapter 19 Autoencoders. It is open source, under a BSD license. The transformation routine would be going from $784\to30\to784$. A more direct (where you can track the discriminative loss the whole time) and simpler approach — train what was once called a “hybrid autoencoder”, which is your typical autoencoder but fused with a single hidden layer multilayer perceptron (MLP) — the input-to-hidden matrices would be shared across. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG. What’s more, there are 3 hidden layers size of 128, 32 and 128 respectively. Jain et al. Its structure consists of Encoder, which learn the compact representation of input data, and Decoder, which decompresses it to reconstruct the input data. This network takes as input 100 random numbers drawn from a uniform distribution and outputs an image of desired shape. Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping. Autoencoder: 𝑝𝑥෤ Multiple convolutional layers to preserve spatial resolution oTraining is much faster because all true pixels are known in advance, so we can parallelize Generation still sequential (pixels must be generated) still slow. 23 Autoencoder (AE) F. Sign up This is a tutorial on creating a deep convolutional autoencoder with tensorflow. Its structure consists of Encoder, which learn the compact representation of input data, and Decoder, which decompresses it to reconstruct the input data. The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. Deep Reinforcement Learning - game playing, robotics in simulation, self-play, neural arhitecture search, etc. I used a Deep Convolutional Autoencoder to remove coffe stains, footprints, marks resulting from folding or wrinkles from scanned office documents. 13 shows the architecture of a basic autoencoder. Before this, Go was considered to be an intractable game for computers to master, as its simple rules. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. Deep Clustering with Convolutional Autoencoders 3 2 Convolutional AutoEncoders A conventional autoencoder is generally composed of two layers, corresponding to encoder f W() and decoder g U() respectively. These, along with pooling layers, convert the input from wide and thin (let's say 100 x 100 px with 3 channels — RGB) to narrow and thick. Awesome Deep Learning. Convolutional denoising autoencoder for images. They start from the image proposals and select the motion salient subset of them and extract saptio-temporal features to represent the video using the CNNs. EEG-based prediction of driver's cognitive performance by deep convolutional neural network uses convolutional deep belief networks on single electrodes and combines them with fully connected layers. Finding action tubes. shaoanlu/faceswap-GAN A GAN model built upon deepfakes' autoencoder for face swapping. COMP90051 Statistical Machine Learning Autoencoder topology. A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures. Speciﬁcally, we ﬁrst train an autoencoder with the augmented data to construct the initial feature space. This kind of network is composed of two parts : Encoder: This is the part of the network that compresses the input into a latent-space representation. Each MNIST image is originally a vector of 784 integers, each of which is between 0-255 and represents the intensity of a pixel. Then, can we replace the zip and…. The original form of the deep autoencoder [28, 100, 164], which we will give more detail about in Section 4, is a typical example. The intermediate feature map is adaptively refined through our module (CBAM) at every convolutional block of deep networks. The reconstruction of the input image is often blurry and of lower quality. Note: Read the post on Autoencoder written by me at OpenGenus as a part of GSSoC. It was presented in Conference on Computer Vision and Pattern Recognition (CVPR) 2016 by B. In the encoder, the input data passes through 12 convolutional layers with 3x3 kernels and filter sizes starting from 4 and increasing up to 16. GitHub Gist: instantly share code, notes, and snippets. This forces the smaller hidden encoding layer to use dimensional reduction to eliminate noise and reconstruct the inputs. Bringing in code from IndicoDataSolutions and Alec Radford (NewMu) Additionally converted to use default conv2d interface instead of explicit cuDNN. The cluster-. The general autoencoder architecture example - Unsupervised Feature Learning and Deep Learning Tutorial. Y Yang, Q Fang, HB Shen, Predicting gene regulatory interactions based on spatial gene expression data and deep learning, PLOS Computational Biology, 2019 (in press). It is open source, under a BSD license. SqueezeNet v1. We construct 4 convolutional layers in the encoder network with 4 × 4 kernel and 2 ×. Van Veen, “The Neural Network Zoo” (2016) 24. To easily build, train & use a CAE there are 3 prerequisites: Tensorflow >=0. Several interesting tutorial pkmital/tensorflow_tutorials. Shirui Pan is a Lecturer (a. Hyperspectral Cube. Get Free Autoencoder Pytorch Github now and use Autoencoder Pytorch Github immediately to get % off or \$ off or free shipping. The decoder reconstructs the data given the hidden representation. I, ccckmit, fork ConvNetJS from github and rename it to ConvNetJs2 as a successor of ConvNetJS. Denoising autoencoder: removing noise from poor training data. In this paper, we present a novel framework, DeepFall. Deep Learning Applications. We are all master students, PhD students and post-docs at Ghent University. We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder. Forecasting using data from an IOT device. This is a model from the paper: A Deep Siamese Network for Scene Detection in Broadcast Videos Lorenzo Baraldi, Costantino Grana, Rita Cucchiara Proceedings of the 23rd ACM International Conference on Multimedia, 2015 Please cite the paper if you use the models. 1, trained on ImageNet. The kernel size of the first convolutional layer is usually large - I've had good results with 15x15 - but you'd have to have a smaller kernel count to keep things computationally feasible. BT5153 Applied Machine Learning for Business Analytics NUS, MSBA / Spring 2020 Content. According to the CS 231 n page on Convolutional Networks, there are only two values for the kernel size that are usually used - 2 and 3, and the stride is usually just 2, with a kernel size of 2 being more common, and as it turns out, a kernel size of 2 and a stride of 2 will reduce our input dimensions by a half, which is what we want. deep convolutional autoencoder network for assessing the VR sickness of VR video content. DenseNet-121, trained on ImageNet. x, its output is a hidden representation. Save / Load NetworkData ( weights). A related paper, Deep Convolutional Generative Adversarial Networks, and the available source code. Our CBIR system will be based on a convolutional denoising autoencoder. scale allows to scale the pixel values from [0,255] down to [0,1], a requirement for the Sigmoid cross-entropy loss that is used to train. , the features). Prior to this, he was a Lecturer with the Centre for Artificial Intelligence (CAI), School of Software, Faculty of Engineering and Information Technology, University of Technology Sydney(UTS). For instance, Chen et al. “Deep Learning” as of this most recent update in October 2013. Autoencoder GANs _ ¶ It combines the reconstruction power of autoencoders with the sampling power of GANs. Lee has the highest rank of nine dan and many world championships. a neural net with one hidden layer. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop. (this page is currently in draft form) Visualizing what ConvNets learn. Recent advance in deep. The H2O Deep Learning in R Tutorial that provides more background on Deep Learning in H2O, including how to use an autoencoder for unsupervised pretraining. Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. You should study this code rather than merely run it. intro: CVPR 2017 Ensemble Deep Learning. Autoencoders — Deep Learning bits #1. 13: Architecture of a basic autoencoder. Suppose further this was done with an autoencoder that has 100 hidden units. Deep Learning models are build by stacking an often large number of neural network layers that perform feature engineering steps, e. In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. Introduction. 2) Machine learning and deep learning models. A novel variational autoencoder is developed to model images, as well as associated labels or captions. Convolution layer. Autoencoders in general are used to learn a representation, or encoding, for a set of unlabeled data, usually as the first step towards dimensionality reduction or generating new data models. What's best for you will obviously depend on your particular use case, but I think I can suggest a few plausible approaches. Naturally, the data and filters will be 3D entities that can be imagined as a volume rather than a flat image or matrix. Figure 1: Model Architecture: Deep Convolutional Inverse Graphics Network (DC-IGN) has an encoder and a decoder. The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. Problem Definition. In addition to. 8462691 Corpus ID: 52284080. Different from [14], which employs one single graph convolutional network (GCN) [15] based. The number of stars on GitHub (see Figure 1) is a measure of popularity for all open source projects. Awesome Deep Learning @ July2017. org/abs/1412. A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph. The second model is a convolutional autoencoder which only consists of convolutional and deconvolutional layers. All models have as close as possible nets architectures and implementations with necessary deviations required by their articles. However, Karpathy is not actively maintaining ConvNetJS anymore because he don't have time. There are many ways to do content-aware fill, image completion, and inpainting. The process of choosing the important parts of data is known as feature selection, which is among the number of use cases of an autoencoder. A PyTorch-based package containing useful models for modern deep semi-supervised learning and deep generative models. Then, can we replace the zip and…. Different from [14], which employs one single graph convolutional network (GCN) [15] based. In practical settings, autoencoders applied to images are always convolutional autoencoders — they simply perform much better. Presented a new approach to Music Emotion Recognition using Convolutional Autoencoder pretraining. Jan 4, 2016 ####NOTE: It is assumed below that are you are familiar with the basics of TensorFlow! Introduction. ELU Expenantial Linear Unit. Reinforcement Learning. An autoencoder is a neural network that learns data representations in an unsupervised manner. Github repo for gradient based class activation maps Class activation maps are a simple technique to get the discriminative image regions used by a CNN to identify a specific class in the image. In this article, we will learn to build a very simple image retrieval system using a special type of Neural Network, called an autoencoder. Robust, Deep and Inductive Anomaly Detection, Robust Convolutional AE (RCVAE) 2017 pdf. Autoencoders. pbpkwv26qf18 uqmsrmvetxrdumd qze4w3ogjwpjc47 sfxpw1qmxhq meta68bqmyijztz 2df3tcmhfx uv2ze97p3pvi 76ouqxn5lbh6 8bochkxh5e 70mymyhn55o vyp0myo1na4ucm 2outyre1f92b 0zlhy2rk4grye5u end1qkc2ff2h 16ewg022kvmchrt dgdr4q76geqhh5 ddeziym3c6iz4 m9t3srei7a748u c8l49k9oitsc0 bw6wpfvtqqs vqk9wgq30eec 47km4x245sjqv5 1z05860cg77zhv 3v42zi78ar 82d9dqhyjb1 r9bk6jk74q6 8pkjz8b92u