Awesome
State-of-the-art result for all Machine Learning Problems
LAST UPDATE: 20th Februray 2019
NEWS: I am looking for a Collaborator esp who does research in NLP, Computer Vision and Reinforcement learning. If you are not a researcher, but you are willing, contact me. Email me: yxt.stoaml@gmail.com
This repository provides state-of-the-art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
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This is an attempt to make one stop for all types of machine learning problems state of the art result. I can not do this alone. I need help from everyone. Please submit the Google form/raise an issue if you find SOTA result for a dataset. Please share this on Twitter, Facebook, and other social media.
This summary is categorized into:
- Supervised Learning
- Semi-supervised Learning
- Computer Vision
- Unsupervised Learning
- Speech
- Computer Vision
- NLP
- Transfer Learning
- Reinforcement Learning
Supervised Learning
NLP
1. Language Modelling
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf'> Language Models are Unsupervised Multitask Learners </a></td> <td align="left"><ul><li> PTB </li><li> WikiText-2 </li></ul></td> <td align="left"><ul><li> Perplexity: 35.76 </li><li> Perplexity: 18.34 </li></ul></td> <td align="left"><a href='https://github.com/openai/gpt-2'>Tensorflow </a></td> <td align="left">2019</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1711.03953.pdf'>BREAKING THE SOFTMAX BOTTLENECK: A HIGH-RANK RNN LANGUAGE MODEL </a></td> <td align="left"><ul><li> PTB </li><li> WikiText-2 </li></ul></td> <td align="left"><ul><li> Perplexity: 47.69 </li><li> Perplexity: 40.68 </li></ul></td> <td align="left"><a href='https://github.com/zihangdai/mos'>Pytorch </a></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1709.07432.pdf'>DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS </a></td> <td align="left"><ul><li> PTB </li><li> WikiText-2 </li></ul></td> <td align="left"><ul><li> Perplexity: 51.1 </li><li> Perplexity: 44.3 </li></ul></td> <td align="left"><a href='https://github.com/benkrause/dynamic-evaluation'>Pytorch </a></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1708.02182.pdf'>Averaged Stochastic Gradient Descent <br/> with Weight Dropped LSTM or QRNN </a></td> <td align="left"><ul><li> PTB </li><li> WikiText-2 </li></ul></td> <td align="left"><ul><li> Perplexity: 52.8 </li><li> Perplexity: 52.0 </li></ul></td> <td align="left"><a href='https://github.com/salesforce/awd-lstm-lm'>Pytorch </a></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1711.00066.pdf'>FRATERNAL DROPOUT </a></td> <td align="left"><ul><li> PTB </li><li> WikiText-2 </li></ul></td> <td align="left"><ul><li> Perplexity: 56.8 </li><li> Perplexity: 64.1 </li></ul></td> <td align="left"> <a href='https://github.com/kondiz/fraternal-dropout'> Pytorch </a> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1703.10722.pdf'>Factorization tricks for LSTM networks </a></td> <td align="left">One Billion Word Benchmark</td> <td align="left"> Perplexity: 23.36</td> <td align="left"><a href='https://github.com/okuchaiev/f-lm'>Tensorflow </a></td> <td align="left">2017</td> </tr> </tbody> </table>2. Machine Translation
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1808.09381v2.pdf'> Understanding Back-Translation at Scale </a></td> <td align="left"> <ul><li>WMT 2014 English-to-French </li><li>WMT 2014 English-to-German </li></ul></td> <td align="left"> <ul><li> BLEU: 45.6 </li><li> BLEU: 35.0 </li></ul> </td> <td align="left"> <ul><li><a href='https://github.com/pytorch/fairseq'>PyTorch</a></li></ul></td> <td align="left">2018</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1711.02132.pdf'>WEIGHTED TRANSFORMER NETWORK FOR MACHINE TRANSLATION</a></td> <td align="left"> <ul><li>WMT 2014 English-to-French </li><li>WMT 2014 English-to-German </li></ul></td> <td align="left"> <ul><li> BLEU: 41.4 </li><li> BLEU: 28.9 </li></ul> </td> <td align="left"> <ul><li><a href=''>NOT FOUND</a></li></ul></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/abs/1706.03762'>Attention Is All You Need</a></td> <td align="left"> <ul><li>WMT 2014 English-to-French </li><li>WMT 2014 English-to-German </li></ul></td> <td align="left"> <ul><li> BLEU: 41.0 </li><li> BLEU: 28.4 </li></ul> </td> <td align="left"> <ul><li><a href='https://github.com/jadore801120/attention-is-all-you-need-pytorch'>PyTorch</a> </li><li> <a href='https://github.com/tensorflow/tensor2tensor'>Tensorflow</a></li></ul></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://einstein.ai/static/images/pages/research/non-autoregressive-neural-mt.pdf'>NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION</a></td> <td align="left"> <ul><li> WMT16 Ro→En </li></ul></td> <td align="left"> <ul><li> BLEU: 31.44 </li></ul> </td> <td align="left"><ul><li><a href='https://github.com/salesforce/nonauto-nmt'>PyTorch</a></ul></li></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/abs/1703.04887'> Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets</a></td> <td align="left"> <ul><li>NIST02 </li><li>NIST03 </li><li>NIST04 </li><li>NIST05 </li></ul></td> <td align="left"><li>38.74 </li><li>36.01 </li><li> 37.54 </li><li>33.76 </li></ul </td> <td align="left"> <ul><li><a href='https://github.com/ngohoanhkhoa/GAN-NMT'>NMTPY</a> </li></ul></td> <td align="left">2017</td> </tr> </tbody> </table>3. Text Classification
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/abs/1705.09207'> Learning Structured Text Representations </a></td> <td align="left">Yelp</td> <td align="left">Accuracy: 68.6</td> <td align="left"> <ul><li><a href='https://github.com/nlpyang/structured'>Tensorflow</a></ul></li></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1710.00519.pdf'>Attentive Convolution</a></td> <td align="left">Yelp</td> <td align="left">Accuracy: 67.36</td> <td align="left"> <ul><li><a href='https://github.com/yinwenpeng/Attentive_Convolution'>Theano</a></ul></li></td> <td align="left">2017</td> </tr> </tbody> </table>4. Natural Language Inference
Leader board:
Stanford Natural Language Inference (SNLI)
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1709.04348.pdf'> NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE </a></td> <td align="left">Stanford Natural Language Inference (SNLI)</td> <td align="left">Accuracy: 88.9</td> <td align="left"><a href='https://github.com/YichenGong/Densely-Interactive-Inference-Network'>Tensorflow</a> </td> <td align="left">2017</td> </tr> <tr> <td><a href=https://arxiv.org/pdf/1810.04805.pdf> BERT-LARGE (ensemble) </a></td> <td align="left">Multi-Genre Natural Language Inference (MNLI)</td> <td align="left"><ul><li>Matched accuracy: 86.7</li><li>Mismatched accuracy: 85.9</td> <td align="left"><ul><li><a href='https://github.com/google-research/bert'>Tensorflow</a></li><li><a href='https://github.com/huggingface/pytorch-pretrained-BERT'>PyTorch</a></li> </td> <td align="left">2018</td> </tr> </tbody> </table>5. Question Answering
Leader Board
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1810.04805.pdf'> BERT-LARGE (ensemble) </a></td> <td align="left">The Stanford Question Answering Dataset</td> <td align="left"><ul><li> Exact Match: 87.4 </li><li> F1: 93.2 </li></ul></td> <td align="left"><ul><li><a href='https://github.com/google-research/bert'>Tensorflow</a></li><li><a href='https://github.com/huggingface/pytorch-pretrained-BERT'>PyTorch</a> </td> <td align="left">2018</td> </tr> </tbody> </table>6. Named entity recognition
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1710.11027.pdf'>Named Entity Recognition in Twitter using Images and Text </a></td> <td align="left">Ritter</td> <td align="left"><ul><li> F-measure: 0.59 </li></ul></td> <td align="left"><a href=''>NOT FOUND</a> </td> <td align="left">2017</td> </tr> </tbody> </table>7. Abstractive Summarization
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Cutting-off redundant repeating generations </br> for neural abstractive summarization | <ul><li>DUC-2004</li><li>Gigaword</li></ul> | <ul><li>DUC-2004</li><ul><li> ROUGE-1: 32.28 </li><li> ROUGE-2: 10.54 </li><li>ROUGE-L: 27.80 </li></ul><li>Gigaword</li><ul><li> ROUGE-1: 36.30 </li><li> ROUGE-2: 17.31 </li><li>ROUGE-L: 33.88 </li></ul></ul> | NOT YET AVAILABLE | 2017 |
| Convolutional Sequence to Sequence | <ul><li>DUC-2004</li><li>Gigaword</li></ul> | <ul><li>DUC-2004</li><ul><li> ROUGE-1: 33.44 </li><li> ROUGE-2: 10.84 </li><li>ROUGE-L: 26.90 </li></ul><li>Gigaword</li><ul><li> ROUGE-1: 35.88 </li><li> ROUGE-2: 27.48 </li><li>ROUGE-L: 33.29 </li></ul></ul> | PyTorch | 2017 |
8. Dependency Parsing
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Globally Normalized Transition-Based Neural Networks | <ul><li>Final CoNLL ’09 dependency parsing </li></ul> | <ul><li> 94.08% UAS accurancy</li> <li>92.15% LAS accurancy</li></ul> | <ul><li>SyntaxNet </li></ul> | <ul><li>2017</li></ul> |
Computer Vision
1. Classification
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1710.09829.pdf'> Dynamic Routing Between Capsules </a></td> <td align="left"> <ul><li> MNIST </li></ul> </td> <td align="left"> <ul><li> Test Error: 0.25±0.005 </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/Sarasra/models/tree/master/research/capsules'>Official Implementation</a> </li><li> <a href='https://github.com/gram-ai/capsule-networks'>PyTorch</a> </li><li> <a href='https://github.com/naturomics/CapsNet-Tensorflow'>Tensorflow</a> </li><li> <a href='https://github.com/XifengGuo/CapsNet-Keras'>Keras</a> </li><li> <a href='https://github.com/soskek/dynamic_routing_between_capsules'>Chainer</a> </li> <li> <a href='https://github.com/loretoparisi/CapsNet'>List of all implementations</a> </li> </ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1102.0183.pdf'> High-Performance Neural Networks for Visual Object Classification </a></td> <td align="left"> <ul><li> NORB </li></ul></td> <td align="left"> <ul><li> Test Error: 2.53 ± 0.40 </li></ul> </td> <td align="left"> <ul><li><a href=''>NOT FOUND</a></ul></li> </td> <td align="left">2011</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1811.06965.pdf'>Giant AmoebaNet with GPipe</a></td> <td align="left"> <ul><li> CIFAR-10 </li> <li> CIFAR-100</li><li> ImageNet-1k</li><li> ...</li></ul></td> <td align="left"> <ul><li> Test Error: 1.0% </li> <li> Test Error: 8.7% </li><li> Top-1 Error 15.7</li><li> ...</li></ul> </td> <td align="left"> <ul><li> <a href=''>NOT FOUND</a> </li></ul> </td> <td align="left">2018</td> </tr> <tr> <td><a href='https://openreview.net/pdf?id=S1NHaMW0b'>ShakeDrop regularization </a></td> <td align="left"> <ul><li> CIFAR-10 </li> <li> CIFAR-100</li></ul></td> <td align="left"> <ul><li> Test Error: 2.31% </li> <li> Test Error: 12.19% </li></ul> </td> <td align="left"> <ul><li> <a href=''>NOT FOUND</a> </li></ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1611.05431.pdf'>Aggregated Residual Transformations for Deep Neural Networks </a></td> <td align="left"> <ul><li> CIFAR-10 </li></ul></td> <td align="left"> <ul><li> Test Error: 3.58% </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/facebookresearch/ResNeXt'>PyTorch</a> </li></ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/abs/1708.04896'> Random Erasing Data Augmentation </a></td> <td align="left"> <ul><li> CIFAR-10 </li> <li> CIFAR-100 </li> <li> Fashion-MNIST </li> </ul></td> <td align="left"> <ul><li> Test Error: 3.08% </li> <li> Test Error: 17.73% </li> <li> Test Error: 3.65% </li> </ul> </td> <td align="left"> <a href='https://github.com/zhunzhong07/Random-Erasing'> Pytorch </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/abs/1709.07634'> EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks </a></td> <td align="left"> <ul><li> CIFAR-10 </li> <li> CIFAR-100 </li> </ul></td> <td align="left"> <ul><li> Test Error: 3.56% </li> <li> Test Error: 16.53% </li> </ul> </td> <td align="left"> <a href='https://github.com/D-X-Y/EraseReLU'> Pytorch </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1710.09829.pdf'> Dynamic Routing Between Capsules </a></td> <td align="left"> <ul><li> MultiMNIST </li></ul></td> <td align="left"> <ul><li> Test Error: 5% </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/gram-ai/capsule-networks'>PyTorch</a> </li><li> <a href='https://github.com/naturomics/CapsNet-Tensorflow'>Tensorflow</a> </li><li> <a href='https://github.com/XifengGuo/CapsNet-Keras'>Keras</a> </li><li> <a href='https://github.com/soskek/dynamic_routing_between_capsules'>Chainer</a> </li><li> <a href='https://github.com/loretoparisi/CapsNet'>List of all implementations</a> </li></ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1707.07012.pdf'>Learning Transferable Architectures for Scalable Image Recognition</a></td> <td align="left"> <ul><li> ImageNet-1k </li></ul></td> <td align="left"> <ul><li> Top-1 Error:17.3 </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet'>Tensorflow</a> </li></ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1709.01507.pdf'>Squeeze-and-Excitation Networks </a></td> <td align="left"> <ul><li> ImageNet-1k </li></ul></td> <td align="left"> <ul><li> Top-1 Error: 18.68 </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/hujie-frank/SENet'>CAFFE</a> </li></ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1611.05431.pdf'>Aggregated Residual Transformations for Deep Neural Networks </a></td> <td align="left"> <ul><li> ImageNet-1k </li></ul></td> <td align="left"> <ul><li> Top-1 Error: 20.4% </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/facebookresearch/ResNeXt'>Torch</a> </li></ul> </td> <td align="left">2016</td> </tr> </tbody> </table>2. Instance Segmentation
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1703.06870.pdf'>Mask R-CNN</a></td> <td align="left"> <ul><li> COCO </li></ul></td> <td align="left"> <ul><li> Average Precision: 37.1% </li></ul> </td> <td align="left"> <ul><li> <a href='https://github.com/facebookresearch/Detectron'>Detectron (Official Version)</a> </li><li> <a href='https://github.com/TuSimple/mx-maskrcnn'>MXNet</a> </li><li> <a href='https://github.com/matterport/Mask_RCNN'>Keras</a> </li><li> <a href='https://github.com/CharlesShang/FastMaskRCNN'>TensorFlow </a> </li></ul> </td> <td align="left">2017</td> </tr> </tbody> </table>3. Visual Question Answering
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/abs/1708.02711'>Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge</a></td> <td align="left"> <ul><li> VQA </li></ul></td> <td align="left"> <ul><li> Overall score: 69 </li></ul> </td> <td align="left"> <ul><li> <a href=''>NOT FOUND</a> </li></ul> </li></ul> </td> <td align="left">2017</td> </tr> </tbody> </table>4. Person Re-identification
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/abs/1708.04896'> Random Erasing Data Augmentation </a></td> <td align="left"> <ul><li> <a href='http://www.liangzheng.org/Project/project_reid.html'> Market-1501 </a> </li> <li> <a href='https://github.com/zhunzhong07/person-re-ranking'> CUHK03-new-protocol </a> </li> <li> <a href='https://github.com/layumi/DukeMTMC-reID_evaluation'> DukeMTMC-reID </a> </li> </ul></td> <td align="left"> <ul><li> Rank-1: 89.13 mAP: 83.93 </li> <li> Rank-1: 84.02 mAP: 78.28 </li> <li> labeled (Rank-1: 63.93 mAP: 65.05) detected (Rank-1: 64.43 mAP: 64.75) </li> </ul> </td> <td align="left"> <a href='https://github.com/zhunzhong07/Random-Erasing'> Pytorch </td> <td align="left">2017</td> </tr> </tbody> </table>Speech
1. ASR
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1708.06073.pdf'>The Microsoft 2017 Conversational Speech Recognition System</a></td> <td align="left"> <ul><li> Switchboard Hub5'00 </li></ul></td> <td align="left"> <ul><li> WER: 5.1 </li></ul> </td> <td align="left"> <ul><li> <a href=''>NOT FOUND</a></li></ul> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1801.00059.pdf'>The CAPIO 2017 Conversational Speech Recognition System</a></td> <td align="left"> <ul><li> Switchboard Hub5'00 </li></ul></td> <td align="left"> <ul><li> WER: 5.0 </li></ul> </td> <td align="left"> <ul><li> <a href=''>NOT FOUND</a></li></ul> </td> <td align="left">2017</td> </tr> </tbody> </table>Semi-supervised Learning
Computer Vision
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='https://arxiv.org/pdf/1507.00677.pdf'> DISTRIBUTIONAL SMOOTHINGWITH VIRTUAL ADVERSARIAL TRAINING </a></td> <td align="left"> <ul><li> SVHN </li><li> NORB </li></ul></td> <td align="left"> <ul><li> Test error: 24.63 </li><li> Test error: 9.88 </li></ul> </td> <td align="left"> <a href='https://github.com/takerum/vat'>Theano</a></td> <td align="left">2016</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1704.03976.pdf'> Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning </a></td> <td align="left"> <ul><li> MNIST </li></ul></td> <td align="left"> <ul><li> Test error: 1.27 </li></ul> </td> <td align="left"> <ul><li><a href=''>NOT FOUND</a></ul></li> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1706.08249.pdf'> Few Shot Object Detection </a></td> <td align="left"> <ul><li> VOC2007 </li><li> VOC2012 </li></ul></td> <td align="left"> <ul><li> mAP : 41.7 </li><li> mAP : 35.4 </li></ul> </td> <td align="left"> <ul><li><a href=''>NOT FOUND</a></ul></li> </td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1701.07717.pdf'> Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro </a></td> <td align="left"> <ul><li> <a href='http://www.liangzheng.org/Project/project_reid.html'> Market-1501 </a> </li> <li> CUHK-03 </li> <li> <a href='https://github.com/layumi/DukeMTMC-reID_evaluation'> DukeMTMC-reID </a> </li> <li> <a href='http://www.vision.caltech.edu/visipedia/CUB-200-2011.html'> CUB-200-2011 </a></li></ul></td> <td align="left"> <ul><li> Rank-1: 83.97 mAP: 66.07 </li> <li> Rank-1: 84.6 mAP: 87.4 </li> <li> Rank-1: 67.68 mAP: 47.13 </li> <li> Test Accuracy: 84.4 </li> </ul> </td> <td align="left"> <a href='https://github.com/layumi/Person-reID_GAN'> Matconvnet </td> <td align="left">2017</td> </tr> </tbody> </table>Unsupervised Learning
Computer Vision
1. Generative Model
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> </tr> <tr> <td><a href='http://research.nvidia.com/sites/default/files/publications/karras2017gan-paper-v2.pdf'> PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION </a></td> <td align="left">Unsupervised CIFAR 10</td> <td align="left">Inception score: 8.80 </td> <td align="left"> <a href='https://github.com/tkarras/progressive_growing_of_gans'>Theano</a></td> <td align="left">2017</td> </tr> </tbody> </table>NLP
Machine Translation
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> <tr> <td><a href='https://arxiv.org/pdf/1711.00043.pdf'>UNSUPERVISED MACHINE TRANSLATION USING MONOLINGUAL CORPORA ONLY</a></td> <td align="left"> <ul><li> Multi30k-Task1(en-fr fr-en de-en en-de) </li></ul></td> <td align="left"> <ul><li> BLEU:(32.76 32.07 26.26 22.74) </li></ul> </td> <td align="left"><ul><li><a href=''>NOT FOUND</a></ul></li></td> <td align="left">2017</td> </tr> <tr> <td><a href='https://arxiv.org/pdf/1804.09057.pdf'>Unsupervised Neural Machine Translation with Weight Sharing</a></td> <td align="left"> <ul><li> WMT14(en-fr fr-en) </li><li> WMT16 (de-en en-de) </li></ul></td> <td align="left"> <ul><li> BLEU:(16.97 15.58) </li> <li> BLEU:(14.62 10.86) </li></ul> </td> <td align="left"><ul><li><a href=''>NOT FOUND</a></ul></li></td> <td align="left">2018</td> </tr> </tbody> </table>Transfer Learning
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> <tr> <td><a href='https://arxiv.org/pdf/1706.05137.pdf'>One Model To Learn Them All</a></td> <td align="left"> <ul><li> WMT EN → DE </li><li> WMT EN → FR (BLEU) </li><li> ImageNet (top-5 accuracy) </li></ul></td> <td align="left"> <ul><li> BLEU: 21.2 </li> <li> BLEU:30.5 </li><li> 86% </li></ul> </td> <td align="left"><ul><li><a href='https://github.com/tensorflow/tensor2tensor'>Tensorflow</a></ul></li></td> <td align="left">2017</td> </tr> </tbody> </table>Reinforcement Learning
<table> <tbody> <tr> <th width="30%">Research Paper</th> <th align="center" width="20%">Datasets</th> <th align="center" width="20%">Metric</th> <th align="center" width="20%">Source Code</th> <th align="center" width="10%">Year</th> <tr> <td><a href='http://www.gwern.net/docs/rl/2017-silver.pdf'>Mastering the game of Go without human knowledge</a></td> <td align="left"> the game of Go </td> <td align="left"> ElO Rating: 5185</td> <td align="left"><ul><li><a href=https://github.com/gcp/leela-zero>C++</a></ul></li></td> <td align="left">2017</td> </tr> </tbody> </table>Email: yxt.stoaml@gmail.com