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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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year).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.

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创建于 2017-11-09 · 更新于 2026-10-06 · 今日第 6408 名
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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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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 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

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

PyTorch

2018

WEIGHTED TRANSFORMER NETWORK FOR MACHINE TRANSLATION

WMT 2014 English-to-French WMT 2014 English-to-German

BLEU: 41.4 BLEU: 28.9

NOT FOUND

2017

Attention Is All You Need

WMT 2014 English-to-French WMT 2014 English-to-German

BLEU: 41.0 BLEU: 28.4

PyTorch Tensorflow

2017

NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION

WMT16 Ro→En

BLEU: 31.44

PyTorch

2017

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

NIST02 NIST03 NIST04 NIST05

38.74 36.01 37.54 33.76 NMTPY

2017

3. Text Classification

Research Paper

Datasets

Metric

Source Code

Year

Learning Structured Text Representations

Yelp

Accuracy: 68.6

Tensorflow

2017

Attentive Convolution

Yelp

Accuracy: 67.36

Theano

2017

4. Natural Language Inference

Leader board:

Stanford Natural Language Inference (SNLI)

MultiNLI

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: 86.7Mismatched accuracy: 85.9

TensorflowPyTorch

2018

5. Question Answering

Leader Board

SQuAD

Research Paper

Datasets

Metric

Source Code

Year

BERT-LARGE (ensemble)

The Stanford Question Answering Dataset

Exact Match: 87.4 F1: 93.2

TensorflowPyTorch

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 DUC-2004Gigaword 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 NOT YET AVAILABLE 2017
Convolutional Sequence to Sequence DUC-2004Gigaword 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 PyTorch 2017

8. Dependency Parsing

Research Paper Datasets Metric Source Code Year
Globally Normalized Transition-Based Neural Networks Final CoNLL ’09 dependency parsing 94.08% UAS accurancy 92.15% LAS accurancy SyntaxNet 2017

Computer Vision

1. Classification

Research Paper

Datasets

Metric

Source Code

Year

Dynamic Routing Between Capsules

MNIST

Test Error: 0.25±0.005

Official Implementation PyTorch Tensorflow Keras Chainer List of all implementations

2017

High-Performance Neural Networks for Visual Object Classification

NORB

Test Error: 2.53 ± 0.40

NOT FOUND

2011

Giant AmoebaNet with GPipe

CIFAR-10 CIFAR-100 ImageNet-1k ...

Test Error: 1.0% Test Error: 8.7% Top-1 Error 15.7 ...

NOT FOUND

2018

ShakeDrop regularization

CIFAR-10 CIFAR-100

Test Error: 2.31% Test Error: 12.19%

NOT FOUND

2017

Aggregated Residual Transformations for Deep Neural Networks

CIFAR-10

Test Error: 3.58%

PyTorch

2017

Random Erasing Data Augmentation

CIFAR-10 CIFAR-100 Fashion-MNIST

      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](https://github.com/zhunzhong07/Random-Erasing)

CIFAR-10 CIFAR-100

      Test Error: 3.56% Test Error: 16.53%

[Pytorch

2017

Dynamic Routing Between Capsules](https://github.com/D-X-Y/EraseReLU)

MultiMNIST

Test Error: 5%

PyTorch Tensorflow Keras Chainer List of all implementations

2017

Learning Transferable Architectures for Scalable Image Recognition

ImageNet-1k

Top-1 Error:17.3

Tensorflow

2017

Squeeze-and-Excitation Networks

ImageNet-1k

Top-1 Error: 18.68

CAFFE

2017

Aggregated Residual Transformations for Deep Neural Networks

ImageNet-1k

Top-1 Error: 20.4%

Torch

2016

2. Instance Segmentation

Research Paper

Datasets

Metric

Source Code

Year

Mask R-CNN

COCO

Average Precision: 37.1%

Detectron (Official Version) MXNet Keras TensorFlow

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

NOT FOUND

2017

4. Person Re-identification

Research Paper

Datasets

Metric

Source Code

Year

Random Erasing Data Augmentation

Market-1501 CUHK03-new-protocol DukeMTMC-reID

      Rank-1: 89.13 mAP: 83.93 Rank-1: 84.02 mAP: 78.28 labeled (Rank-1: 63.93 mAP: 65.05) detected (Rank-1: 64.43 mAP: 64.75)

[Pytorch

2017

Speech

Speech SOTA

1. ASR

Research Paper

Datasets

Metric

Source Code

Year

The Microsoft 2017 Conversational Speech Recognition System](https://github.com/zhunzhong07/Random-Erasing)

Switchboard Hub5'00

WER: 5.1

NOT FOUND

2017

The CAPIO 2017 Conversational Speech Recognition System

Switchboard Hub5'00

WER: 5.0

NOT FOUND

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

NOT FOUND

2017

Few Shot Object Detection

VOC2007 VOC2012

mAP : 41.7 mAP : 35.4

NOT FOUND

2017

Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

Market-1501 CUHK-03 DukeMTMC-reID CUB-200-2011

      Rank-1: 83.97 mAP: 66.07 Rank-1: 84.6 mAP: 87.4 Rank-1: 67.68 mAP: 47.13           Test Accuracy: 84.4

[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](https://github.com/layumi/Person-reID_GAN)

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)

NOT FOUND

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)

NOT FOUND

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%

Tensorflow

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

C++

2017

Email: [email protected]