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https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems
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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems
Last synced: 3 months ago
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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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
- Host: GitHub
- URL: https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems
- Owner: RedditSota
- License: apache-2.0
- Created: 2017-11-09T01:21:40.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-25T14:09:52.000Z (over 5 years ago)
- Last Synced: 2024-10-14T21:21:51.676Z (3 months ago)
- Homepage:
- Size: 147 KB
- Stars: 8,948
- Watchers: 872
- Forks: 1,314
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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: [email protected]
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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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.
You can also submit this [Google Form](https://docs.google.com/forms/d/e/1FAIpQLSe_fFZVCeCVRGGgOQIpoQSXY7mZWynsx7g6WxZEVpO5vJioUA/viewform?embedded=true) if you are new to Github.
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](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#supervised-learning)
- [Speech](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#speech)
- [Computer Vision](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#computer-vision)
- [NLP](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#nlp)
- [Semi-supervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#semi-supervised-learning)
- Computer Vision
- [Unsupervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#unsupervised-learning)
- Speech
- Computer Vision
- [NLP](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems/blob/master/README.md#nlp-1)
- [Transfer Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#transfer-learning)
- [Reinforcement Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#reinforcement-learning)
## Supervised Learning
### NLP
#### 1. Language Modelling
Research Paper
Datasets
Metric
Source Code
Year
Language Models are Unsupervised Multitask Learners
- PTB
- WikiText-2
- Perplexity: 35.76
- Perplexity: 18.34
Tensorflow
2019
BREAKING THE SOFTMAX BOTTLENECK: A HIGH-RANK RNN LANGUAGE MODEL
- PTB
- WikiText-2
- Perplexity: 47.69
- Perplexity: 40.68
Pytorch
2017
DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS
- PTB
- WikiText-2
- Perplexity: 51.1
- Perplexity: 44.3
Pytorch
2017
Averaged Stochastic Gradient Descent
with Weight Dropped LSTM or QRNN
- PTB
- WikiText-2
- Perplexity: 52.8
- Perplexity: 52.0
Pytorch
2017
FRATERNAL DROPOUT
- PTB
- WikiText-2
- Perplexity: 56.8
- Perplexity: 64.1
Pytorch
2017
Factorization tricks for LSTM networks
One Billion Word Benchmark
Perplexity: 23.36
Tensorflow
2017
#### 2. Machine Translation
Research Paper
Datasets
Metric
Source Code
Year
Understanding Back-Translation at Scale
- WMT 2014 English-to-French
- WMT 2014 English-to-German
- BLEU: 45.6
- BLEU: 35.0
2018
WEIGHTED TRANSFORMER NETWORK FOR
MACHINE TRANSLATION
- WMT 2014 English-to-French
- WMT 2014 English-to-German
- BLEU: 41.4
- BLEU: 28.9
2017
Attention Is All You Need
- WMT 2014 English-to-French
- WMT 2014 English-to-German
- BLEU: 41.0
- BLEU: 28.4
2017
NON-AUTOREGRESSIVE
NEURAL MACHINE TRANSLATION
- WMT16 Ro→En
- BLEU: 31.44
2017
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
- NIST02
- NIST03
- NIST04
- NIST05
2017
#### 3. Text Classification
Research Paper
Datasets
Metric
Source Code
Year
Learning Structured Text Representations
Yelp
Accuracy: 68.6
2017
Attentive Convolution
Yelp
Accuracy: 67.36
2017
#### 4. Natural Language Inference
Leader board:
[Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/)
[MultiNLI](https://www.kaggle.com/c/multinli-matched-open-evaluation/leaderboard)
Research Paper
Datasets
Metric
Source Code
Year
NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE
Stanford Natural Language Inference (SNLI)
Accuracy: 88.9
Tensorflow
2017
BERT-LARGE (ensemble)
Multi-Genre Natural Language Inference (MNLI)
- Matched accuracy: 86.7
- Mismatched accuracy: 85.9
2018
#### 5. Question Answering
Leader Board
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
Research Paper
Datasets
Metric
Source Code
Year
BERT-LARGE (ensemble)
The Stanford Question Answering Dataset
- Exact Match: 87.4
- F1: 93.2
2018
#### 6. Named entity recognition
Research Paper
Datasets
Metric
Source Code
Year
Named Entity Recognition in Twitter using Images and Text
Ritter
- F-measure: 0.59
NOT FOUND
2017
#### 7. Abstractive Summarization
Research Paper | Datasets | Metric | Source Code | Year
------------ | ------------- | ------------ | ------------- | -------------
[Cutting-off redundant repeating generations for neural abstractive summarization](https://aclanthology.info/pdf/E/E17/E17-2047.pdf) |
- DUC-2004
- Gigaword
- DUC-2004
- ROUGE-1: **32.28**
- ROUGE-2: 10.54
- ROUGE-L: **27.80**
- Gigaword
- ROUGE-1: **36.30**
- ROUGE-2: 17.31
- ROUGE-L: **33.88**
[Convolutional Sequence to Sequence](https://arxiv.org/pdf/1705.03122.pdf) |
- DUC-2004
- Gigaword
- DUC-2004
- ROUGE-1: 33.44
- ROUGE-2: **10.84**
- ROUGE-L: 26.90
- Gigaword
- ROUGE-1: 35.88
- ROUGE-2: 27.48
- ROUGE-L: 33.29
#### 8. Dependency Parsing
Research Paper | Datasets | Metric | Source Code | Year
------------ | ------------- | ------------ | ------------- | -------------
[Globally Normalized Transition-Based Neural Networks](https://arxiv.org/pdf/1603.06042.pdf) |
- Final CoNLL ’09 dependency parsing
- 94.08% UAS accurancy
- 92.15% LAS accurancy
- [SyntaxNet](https://github.com/tensorflow/models/tree/master/research/syntaxnet)
- 2017
### Computer Vision
#### 1. Classification
Research Paper
Datasets
Metric
Source Code
Year
Dynamic Routing Between Capsules
- MNIST
- Test Error: 0.25±0.005
2017
High-Performance Neural Networks for Visual Object Classification
- NORB
- Test Error: 2.53 ± 0.40
2011
Giant AmoebaNet with GPipe
- CIFAR-10
- CIFAR-100
- ImageNet-1k
- ...
- Test Error: 1.0%
- Test Error: 8.7%
- Top-1 Error 15.7
- ...
2018
ShakeDrop regularization
- CIFAR-10
- CIFAR-100
- Test Error: 2.31%
- Test Error: 12.19%
2017
Aggregated Residual Transformations for Deep Neural Networks
- CIFAR-10
- Test Error: 3.58%
2017
Random Erasing Data Augmentation
- CIFAR-10
- CIFAR-100
- Fashion-MNIST
  Â
- Test Error: 3.08%
- Test Error: 17.73%
- Test Error: 3.65%
Pytorch
2017
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
- CIFAR-10
- CIFAR-100
  Â
- Test Error: 3.56%
- Test Error: 16.53%
Pytorch
2017
Dynamic Routing Between Capsules
- MultiMNIST
- Test Error: 5%
2017
Learning Transferable Architectures for Scalable Image Recognition
- ImageNet-1k
- Top-1 Error:17.3
2017
Squeeze-and-Excitation Networks
- ImageNet-1k
- Top-1 Error: 18.68
2017
Aggregated Residual Transformations for Deep Neural Networks
- ImageNet-1k
- Top-1 Error: 20.4%
2016
#### 2. Instance Segmentation
Research Paper
Datasets
Metric
Source Code
Year
Mask R-CNN
- COCO
- Average Precision: 37.1%
2017
#### 3. Visual Question Answering
Research Paper
Datasets
Metric
Source Code
Year
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
- VQA
- Overall score: 69
2017
#### 4. Person Re-identification
Research Paper
Datasets
Metric
Source Code
Year
Random Erasing Data Augmentation
  Â
- Rank-1: 89.13 mAP:Â 83.93
- Rank-1: 84.02 mAP:Â 78.28
- labeled (Rank-1: 63.93 mAP:Â 65.05) detected (Rank-1: 64.43 mAP:Â 64.75)
Pytorch
2017
### Speech
[Speech SOTA](https://github.com/syhw/wer_are_we)
#### 1. ASR
Research Paper
Datasets
Metric
Source Code
Year
The Microsoft 2017 Conversational Speech Recognition System
- Switchboard Hub5'00
- WER: 5.1
2017
The CAPIO 2017 Conversational Speech Recognition System
- Switchboard Hub5'00
- WER: 5.0
2017
## Semi-supervised Learning
#### Computer Vision
Research Paper
Datasets
Metric
Source Code
Year
DISTRIBUTIONAL SMOOTHINGWITH VIRTUAL ADVERSARIAL TRAINING
- SVHN
- NORB
- Test error: 24.63
- Test error: 9.88
Theano
2016
Virtual Adversarial Training:
a Regularization Method for Supervised and
Semi-supervised Learning
- MNIST
- Test error: 1.27
2017
Few Shot Object Detection
- VOC2007
- VOC2012
- mAP : 41.7
- mAP : 35.4
2017
Unlabeled Samples Generated by GAN
Improve the Person Re-identification Baseline in vitro
- Market-1501
- CUHK-03
- DukeMTMC-reID
- CUB-200-2011
  Â
- Rank-1: 83.97 mAP:Â 66.07
- Rank-1: 84.6 mAP:Â 87.4
- Rank-1: 67.68 mAP:Â 47.13
- Test Accuracy: 84.4
    Â
Matconvnet
2017
## Unsupervised Learning
#### Computer Vision
##### 1. Generative Model
Research Paper
Datasets
Metric
Source Code
Year
PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
Unsupervised CIFAR 10
Inception score: 8.80
Theano
2017
### NLP
#### Machine Translation
Research Paper
Datasets
Metric
Source Code
Year
UNSUPERVISED MACHINE TRANSLATION
USING MONOLINGUAL CORPORA ONLY
- Multi30k-Task1(en-fr fr-en de-en en-de)
- BLEU:(32.76 32.07 26.26 22.74)
2017
Unsupervised Neural Machine Translation with Weight Sharing
- WMT14(en-fr fr-en)
- WMT16 (de-en en-de)
- BLEU:(16.97 15.58)
- BLEU:(14.62 10.86)
2018
## Transfer Learning
Research Paper
Datasets
Metric
Source Code
Year
One Model To Learn Them All
- WMT EN → DE
- WMT EN → FR (BLEU)
- ImageNet (top-5 accuracy)
- BLEU: 21.2
- BLEU:30.5
- 86%
2017
## Reinforcement Learning
Research Paper
Datasets
Metric
Source Code
Year
Mastering the game of Go without human knowledge
the game of Go
ElO Rating: 5185
2017
Email: [email protected]