deep-learning-sota
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State-of-the-art results for deep learning tasks in various fields.
Deep Learning SotA
Note: This repository is no longer under support. Please refer to websites such as Paper with Code, which provide more comprehensive and up-to-date information on SOTA models. This repository is in archive mode now.
This repository lists the state-of-the-art results for mainstream deep learning tasks. We do our best to keep it up to date. If you do find a task's SotA result is outdated or missing, please raise an issue (with: title of paper, dataset, metric, source code, and year).
This summary is categorized into:
- Computer Vision
- Speech
- NLP
- Contact
Computer Vision
Classification
| Dataset | Type | Top-1 accuracy | Method | Paper | Code |
|---|---|---|---|---|---|
| ImageNet | ResNet-50 | 78.35% | ResNet-50 + DropBlock + label smoothing | DropBlock: A Regularization Method for Convolutional Neural Networks | |
| ImageNet | Single model | 82.52% | AmoebaNet-B + DropBlock | DropBlock: A Regularization Method for Convolutional Neural Networks |
Object Detection
| Dataset | Type | AP | Method | Paper | Code |
|---|---|---|---|---|---|
| MS-COCO 2017 | ResNet-101 | 43.4 | D-RFCN + SNIP + ResNet-101 | An Analysis of Scale Invariance in Object Detection - SNIP | |
| MS-COCO 2017 | Single model | 45.7 | D-RFCN + SNIP + DPN-98 | An Analysis of Scale Invariance in Object Detection - SNIP |
Instance Segmentation
| Dataset | Type | AP | Method | Paper | Code |
|---|---|---|---|---|---|
| MS-COCO 2018 | Ensemble | 48.6 | mmdet + FishNet, 5 models | - | PyTorch |
Visual Question Answering
| Dataset | Type | Score | Method | Paper | Code |
|---|---|---|---|---|---|
| VQA | Ensemble | 72.41 | Pythia | Pythia v0.1: The Winning Entry to the VQA Challenge 2018 | PyTorch |
Person Re-identification
| Dataset | Type | Rank-1 accuracy | Method | Paper | Code |
|---|---|---|---|---|---|
| Market-1501 | Supervised single-query | 91.2% | Pixel-level attention + region-level attention + joint feature learning | Harmonious Attention Network for Person Re-Identification | |
| Market-1501 | Supervised multi-query | 93.8% | Pixel-level attention + region-level attention + joint feature learning + multi-query | Harmonious Attention Network for Person Re-Identification | |
| DukeMTMC-reID | Supervised single-query | 85.95% | SPReID | Human Semantic Parsing for Person Re-identification |
NLP
Language Modelling
| Dataset | Type | Perplexity | Method | Paper | Code |
|---|---|---|---|---|---|
| Penn Tree Bank | 47.69 | MoS | Breaking the Softmax Bottleneck: A High-Rank RNN Language Model | PyTorch | |
| WikiText-2 | 40.68 | MoS | Breaking the Softmax Bottleneck: A High-Rank RNN Language Model | PyTorch |
Machine Translation
| Dataset | Type | BLEU | Method | Paper | Code |
|---|---|---|---|---|---|
| WMT 2014 English-to-French | 41.4 | Weighted Transformer | Weighted Transformer Network for Machine Translation | ||
| WMT 2014 English-to-German | 28.9 | Weighted Transformer | Weighted Transformer Network for Machine Translation |
Text Classification
| Dataset | Type | Accuracy | Method | Paper | Code |
|---|---|---|---|---|---|
| Yelp | 68.6% | Learning Structured Text Representations | TensorFlow |
Natural Language Inference
| Dataset | Type | Accuracy | Method | Paper | Code |
|---|---|---|---|---|---|
| Stanford Natural Language Inference (SNLI) | Single | 89.9% | GPT | Improving Language Understanding by Generative Pre-Training | |
| Stanford Natural Language Inference (SNLI) | Emsemble | 90.1% | Semantic Sentence Matching with Densely-Connected Recurrent and Co-Attentive Information | ||
| MultiNLI | Emsemble | 86.7% | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |
Question Answering
| Dataset | Type | F1 | Method | Paper | Code |
|---|---|---|---|---|---|
| SQuAD 2.0 | Single model | 83.061 | BERT-large | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |
Named Entity Recognition
| Dataset | Type | F1 | Method | Paper | Code |
|---|---|---|---|---|---|
| CoNLL-2003 | Single model | 92.8 | BERT-large | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |
Speech
Acoustic Speech Recognition
| Dataset | Type | WER | Method | Paper | Code |
|---|---|---|---|---|---|
| Switchboard Hub5'00 | Ensemble | 5.0 | biLSTM + CNN + Dense, 8 models | The CAPIO 2017 Conversational Speech Recognition System |
Contact
Email: [email protected]
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