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A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch

https://github.com/sksq96/pytorch-vaeTags | variational-autoencoder vae convolutional-neural-networks |

Implementation | Jupyter Notebook |

License | Public |

Platform |

Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images. Run python train.py from the command line to train from scratch and experiment with different settings.

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

vae gan pytorch tensorflow generative-model machine-learning rbm restricted-boltzmann-machineImplementation of the method described in our Arxiv paper. We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.

A PyTorch-based package containing useful models for modern deep semi-supervised learning and deep generative models. Want to jump right into it? Look into the notebooks. 2018.04.17 - The Gumbel softmax notebook has been added to show how you can use discrete latent variables in VAEs. 2018.02.28 - The β-VAE notebook was added to show how VAEs can learn disentangled representations.

semi-supervised-learning pytorch generative-modelsVGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images when constructing a new model. This information is needed to determine the input size of fully-connected layers. See examples/cifar10.py file (requires PyTorch 0.4).

pytorch deep-learning convolutional-neural-networks pretrained-modelsNiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

tensorflow distributed ml neural-network python2 python3 pip deep-neural-networks deep-learning convolutional-neural-networks medical-imaging medical-image-computing medical-image-processing medical-images segmentation gan autoencoder medical-image-analysis image-guided-therapySome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnThis repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included by ModelDepot. We also provide Torch implementation and MXNet implementation. Image Style Transfer Using Convolutional Neural Networks by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.

style-transfer deep-neural-networks real-timeDARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arXiv:1806.09055. NOTE: PyTorch 0.4 is not supported at this moment and would lead to OOM.

deep-learning automl image-classification language-modeling pytorch convolutional-networks recurrent-networks neural-architecture-searchPyTorch implementation of Fully Convolutional Networks. See VOC example.

pytorch computer-vision deep-learning semantic-segmentation convolutional-networks fcn fcn8sPyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks (i.e., networks that utilise dynamic control flow like if statements and while loops). It supports GPU acceleration, distributed training, various optimisations, and plenty more neat features. These are some notes on how I think about using PyTorch, and don't encompass all parts of the library or every best practice, but may be helpful to others. Neural networks are a subclass of computation graphs. Computation graphs receive input data, and data is routed to and possibly transformed by nodes which perform processing on the data. In deep learning, the neurons (nodes) in neural networks typically transform data with parameters and differentiable functions, such that the parameters can be optimised to minimise a loss via gradient descent. More broadly, the functions can be stochastic, and the structure of the graph can be dynamic. So while neural networks may be a good fit for dataflow programming, PyTorch's API has instead centred around imperative programming, which is a more common way for thinking about programs. This makes it easier to read code and reason about complex programs, without necessarily sacrificing much performance; PyTorch is actually pretty fast, with plenty of optimisations that you can safely forget about as an end user (but you can dig in if you really want to).

deep-learningThis package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow. from cadl import and then pressing tab to see the list of available modules.

deep-learning neural-network tutorial mooc gan vae vae-gan pixelcnn wavenet magenta nsynth tensorflow celeba cyclegan dcgan word2vec glove autoregressive conditional courseThis repository contains the code release for our paper titled as "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks". The link to the paper is provided as well. The code has been developed using TensorFlow. The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.

convolutional-neural-networks deep-learning speaker-recognition 3dNNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs. NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives leveraged in leading deep learning frameworks, such as PyTorch, Caffe2, MXNet, tiny-dnn, Caffe, Torch, and Darknet.

neural-network neural-networks convolutional-layers inference high-performance high-performance-computing simd cpu multithreading fast-fourier-transform winograd-transform matrix-multiplicationThis repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translationIn these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learningThis repository contains the demo code for the CVPR'17 paper Network Dissection: Quantifying Interpretability of Deep Visual Representations. You can use this code with naive Caffe, with matcaffe and pycaffe compiled. We also provide a PyTorch wrapper to apply NetDissect to probe networks in PyTorch format. There are dissection results for several networks at the project page. Code to run network dissection on an arbitrary deep convolutional neural network provided as a Caffe deploy.prototxt and .caffemodel. The script rundissect.sh runs all the needed phases.

GPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

regl cnn digit-recognition demo gpu webgl convolutional-neural-networks gpgpu deep-learning glsl digit recognition mnist convolutional neural network networksA comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! ðŸ˜€)

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-networkMichaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Neural Information Processing Systems (NIPS), 2016. Clone this repository.

convolutional-neural-networks deep-learning graphs graph-signal-processing
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