Deep Learning, NLP, NMT, AI, ML
Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A3C on InvertedPendulum(MuJoCo)): Sample on-line plotting while training an A3C agent on Pong (with 16 learner processes): Sample loggings while training a DQN agent on CartPole (we use WARNING as the logging level currently to get rid of the INFO printouts from visdom):
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Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhevsky, et al. in their famous paper ImageNet Classification with Deep Convolutional Neural Networks. Famous models such as AlexNet, VGG-16, ResNet-50, etc. have scored state of the art results on image classfication datasets such as ImageNet and CIFAR-10. We present an application of CNN’s to the task of classifying trees by images of their leaves; specifically all 185 types of trees […]
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New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that enables fast and memory-efficient training. We provide PyTorch implementations for both unpaired and paired image-to-image translation. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang. This PyTorch implementation produces results comparable to or better than our original Torch software. If you would like to reproduce the same results as in the papers, check out the original CycleGAN Torch and pix2pix Torch […]
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@inproceedings{mnih2016asynchronous, title={Asynchronous methods for deep reinforcement learning}, author={Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy P and Harley, Tim and Silver, David and Kavukcuoglu, Koray}, booktitle={International Conference on Machine Learning}, year={2016}} This repository contains an implementation of Adavantage async Actor-Critic (A3C) in PyTorch based on the original paper by the authors and the PyTorch implementation by Ilya Kostrikov. A3C is the state-of-art Deep Reinforcement Learning method.
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This 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. Tabe of content MSG-Net Multi-style Generative Network for Real-time Transfer [arXiv] [project] Hang Zhang, Kristin Dana @article{zhang2017multistyle, title={Multi-style Generative Network for Real-time Transfer}, author={Zhang, Hang and Dana, Kristin}, journal={arXiv preprint arXiv:1703.06953}, year={2017} } Stylize Images Using Pre-trained MSG-Net Download the pre-trained model
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DeepLab resnet v2 model implementation in pytorch. The architecture of deepLab-ResNet has been replicated exactly as it is from the caffe implementation. This architecture calculates losses on input images over multiple scales ( 1x, 0.75x, 0.5x ). Losses are calculated individually over these 3 scales. In addition to these 3 losses, one more loss is calculated after merging the output score maps on the 3 scales. These 4 losses are added to calculate the total loss. Updates 18 July 2017 […]
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This is a PyTorch implementation of the Transformer model in “Attention is All You Need” (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017). A novel sequence to sequence framework utilizes the self-attention mechanism, instead of Convolution operation or Recurrent structure, and achieve the state-of-the-art performance on WMT 2014 English-to-German translation task. (2017/06/12) The official Tensorflow Implementation can be found in: tensorflow/tensor2tensor. To learn more about self-attention mechanism, you could […]
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This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train and infer using them. Using this code you can train: Neural-machine-translation (NMT) models Language models Image to caption generation Skip-thought sentence representations And more… Installation git clone –recursive https://github.com/eladhoffer/seq2seq.pytorch cd seq2seq.pytorch; python setup.py develop Models Models currently available: Datasets Datasets currently available: All datasets can be tokenized using 3 available segmentation methods: Character based segmentation Word based segmentation Byte-pair-encoding […]
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This project is still in progress. If you are looking for the working code, use BEGAN-tensorflow. Requirements Usage First download CelebA datasets with: $ apt-get install p7zip-full # ubuntu $ brew install p7zip # Mac $ python download.py or you can use your own dataset by placing images like: data └── YOUR_DATASET_NAME ├── xxx.jpg (name doesn’t matter) ├── yyy.jpg └── … To train a model:
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A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If you’re interested in C++ inference, move HERE Results Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Accuracy 54000 GTX 1080 Ti 512 0.16 100 625 0.9 ~1700 95.65% Sample $ python infer.py -c=./logs/model-54000.pth ./images/test-75.png length: 2 digits: 7 5 10 10 10
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