wxya（个人网站）SOTA 模型
一、图像分类SOTA模型（15个）
1.模型：AlexNet
论文题目：Imagenet Classification with Deep Convolution Neural Network
2.模型：VGG
论文题目：Very Deep Convolutional Networks for Large-Scale Image Recognition
3.模型：GoogleNet
论文题目：Going Deeper with Convolutions
4.模型：ResNet
论文题目：Deep Residual Learning for Image Recognition
5.模型：ResNeXt
论文题目：Aggregated Residual Transformations for Deep Neural Networks
6.模型：DenseNet
论文题目：Densely Connected Convolutional Networks
7.模型：MobileNet
论文题目：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
8.模型：SENet
论文题目：Squeeze-and-Excitation Networks
9.模型：DPN
论文题目：Dual Path Networks
10.模型：IGC V1
论文题目：Interleaved Group Convolutions for Deep Neural Networks
11.模型：Residual Attention Network
论文题目：Residual Attention Network for Image Classification
12.模型：ShuffleNet
论文题目：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
13.模型：MnasNet
论文题目：MnasNet: Platform-Aware Neural Architecture Search for Mobile
14.模型：EfficientNet
论文题目：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
15.模型：NFNet
论文题目：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applic
二、文本分类SOTA模型（12个）
1.模型：RAE
论文题目：Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
2.模型：DAN
论文题目：Deep Unordered Composition Rivals Syntactic Methods for Text Classification
3.模型：TextRCNN
论文题目：Recurrent Convolutional Neural Networks for Text Classification
4.模型：Multi-task
论文题目：Recurrent Neural Network for Text Classification with Multi-Task Learning
5.模型：DeepMoji
论文题目：Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
6.模型：RNN-Capsule
论文题目：Investigating Capsule Networks with Dynamic Routing for Text Classification
7.模型：TextCNN
论文题目：Convolutional neural networks for sentence classification
8.模型：DCNN
论文题目：A convolutional neural network for modelling sentences
9.模型：XML-CNN
论文题目：Deep learning for extreme multi-label text classification
10.模型：TextCapsule
论文题目：Investigating capsule networks with dynamic routing for text classification
11.模型：Bao et al.
论文题目：Few-shot Text Classification with Distributional Signatures
12.模型：AttentionXML
论文题目：AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
三、文本摘要SOTA模型（17个）
1.模型：CopyNet
论文题目：Incorporating Copying Mechanism in Sequence-to-Sequence Learning
2.模型：SummaRuNNer
论文题目：SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documen
3.模型：SeqGAN
论文题目：SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
4.模型：Latent Extractive
论文题目：Neural latent extractive document summarization
5.模型：NEUSUM
论文题目：Neural Document Summarization by Jointly Learning to Score and Select Sentences
6.模型：BERTSUM
论文题目：Text Summarization with Pretrained Encoders
7.模型：BRIO
论文题目：BRIO: Bringing Order to Abstractive Summarization
8.模型：NAM
论文题目：A Neural Attention Model for Abstractive Sentence Summarization
9.模型：RAS
论文题目：Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
10.模型：PGN
论文题目：Get To The Point: Summarization with Pointer-Generator Networks
11.模型：Re3Sum
论文题目：Retrieve, rerank and rewrite: Soft template based neural summarization
12.模型：MTLSum
论文题目：Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation
13.模型：KGSum
论文题目：Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization
14.模型：PEGASUS
论文题目：PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
15.模型：FASum
论文题目：Enhancing Factual Consistency of Abstractive Summarization
16.模型：RNN（ext） + ABS + RL + Rerank
论文题目：Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
17.模型：BottleSUM
论文题目：BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
四、图像生成SOTA模型（16个）
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- A Style-Based Generator Architecture for Generative Adversarial Networks
- Analyzing and Improving the Image Quality of StyleGAN
- Alias-Free Generative Adversarial Networks
- Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
- A Contrastive Learning Approach for Training Variational Autoencoder Priors
- StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
- Diffusion-GAN: Training GANs with Diffusion
- Improved Training of Wasserstein GANs
- Self-Attention Generative Adversarial Networks
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation
- LOGAN: Latent Optimisation for Generative Adversarial Networks
- A U-Net Based Discriminator for Generative Adversarial Networks
- Instance-Conditioned GAN
- Conditional GANs with Auxiliary Discriminative Classifier
五、视频生成SOTA模型（15个）
- Temporal Generative Adversarial Nets with Singular Value Clipping
- Generating Videos with Scene Dynamics
- MoCoGAN: Decomposing Motion and Content for Video Generation
- Stochastic Video Generation with a Learned Prior
- Video-to-Video Synthesis
- Probabilistic Video Generation using Holistic Attribute Control
- ADVERSARIAL VIDEO GENERATION ON COMPLEX DATASETS
- Sliced Wasserstein Generative Models
- Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN
- Latent Neural Differential Equations for Video Generation
- VideoGPT: Video Generation using VQ-VAE and Transformers
- Diverse Video Generation using a Gaussian Process Trigger
- NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
- StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
- Video Diffusion Models
六、强化学习SOTA模型（13个）
- Playing Atari with Deep Reinforcement Learning
- Deep Reinforcement Learning with Double Q-learning
- Continuous control with deep reinforcement learning
- Asynchronous Methods for Deep Reinforcement Learning
- Proximal Policy Optimization Algorithms
- Hindsight Experience Replay
- Emergence of Locomotion Behaviours in Rich Environments
- ImplicitQuantile Networks for Distributional Reinforcement Learning
- Imagination-Augmented Agents for Deep Reinforcement Learning
- Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
- Model-based value estimation for efficient model-free reinforcement learning
- Model-ensemble trust-region policy optimization
- Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
七、语音合成SOTA模型（19个）
- TTS Synthesis with Bidirectional LSTM based Recurrent Neural Networks
- WaveNet: A Generative Model for Raw Audio
- SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
- Char2Wav: End-to-end speech synthesis
- Deep Voice: Real-time Neural Text-to-Speech
- Parallel WaveNet: Fast High-Fidelity Speech Synthesis
- Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework
- Tacotron: Towards End-to-End Speech Synthesis
- VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
- Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
- Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
- Deep Voice 3: Scaling text-to-speech with convolutional sequence learning
- ClariNet Parallel Wave Generation in End-to-End Text-to-Speech
- LPCNET: IMPROVING NEURAL SPEECH SYNTHESIS THROUGH LINEAR PREDICTION
- Neural Speech Synthesis with Transformer Network
- Glow-TTS：A Generative Flow for Text-to-Speech via Monotonic Alignment Search
- FLOW-TTS: A NON-AUTOREGRESSIVE NETWORK FOR TEXT TO SPEECH BASED ON FLOW
- Conditional variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
- PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
八、机器翻译SOTA模型（18个）
- Neural machine translation by jointly learning to align and translate
- Multi-task Learning for Multiple Language Translation
- Effective Approaches to Attention-based Neural Machine Translation
- A Convolutional Encoder Model for Neural Machine Translation
- Attention is All You Need
- Decoding with Value Networks for Neural Machine Translation
- Unsupervised Neural Machine Translation
- Phrase-based & Neural Unsupervised Machine Translation
- Addressing the Under-translation Problem from the Entropy Perspective
- Modeling Coherence for Discourse Neural Machine Translation
- Cross-lingual Language Model Pretraining
- MASS: Masked Sequence to Sequence Pre-training for Language Generation
- FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
- Multilingual Denoising Pre-training for Neural Machine Translation
- Incorporating BERT into Neural Machine Translation
- Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information
- Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
- Universal Conditional Masked Language Pre-training for Neural Machine Translation
九、文本生成SOTA模型（10个）
- Sequence to sequence learning with neural networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Neural machine translation by jointly learning to align and translate
- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
- Attention is all you need
- Improving language understanding by generative pre-training
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Cross-lingual Language Model Pretraining
- Language Models are Unsupervised Multitask Learners
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
十、语音识别SOTA模型（12个）
- A Neural Probabilistic Language Model
- Recurrent neural network based language model
- Lstm neural networks for language modeling
- Hybrid speech recognition with deep bidirectional lstm
- Attention is all you need
- Improving language understanding by generative pre- training
- Bert: Pre-training of deep bidirectional transformers for language understanding
- Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
- Lstm neural networks for language modeling
- Feedforward sequential memory networks: A new structure to learn long-term dependency
- Convolutional, long short-term memory, fully connected deep neural networks
- Highway long short-term memory RNNs for distant speech recognition
十一、目标检测SOTA模型（16个）
- Rich feature hierarchies for accurate object detection and semantic segmentation
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Fast R-CNN
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Training Region-based Object Detectors with Online Hard Example Mining
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- Mask R-CNN
- You Only Look Once: Unified, Real-Time Object Detection
- SSD: Single Shot Multibox Detector
- Feature Pyramid Networks for Object Detection
- Focal Loss for Dense Object Detection
- Accurate Single Stage Detector Using Recurrent Rolling Convolution
- CornerNet: Detecting Objects as Paired Keypoints
- M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
- Fully Convolutional One-Stage Object Detection
- ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
十二、推荐系统SOTA模型（18个）
- Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
- Deep Neural Networks for YouTube Recommendations
- Self-Attentive Sequential Recommendation
- Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- Learning Tree-based Deep Model for Recommender Systems
- Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
- PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
- Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
- Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
- Field-aware Factorization Machines for CTR Prediction
- Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction
- Product-based Neural Networks for User Response Prediction
- Wide & Deep Learning for Recommender Systems
- Deep & Cross Network for Ad Click Predictions
- xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
- Deep Interest Network for Click-Through Rate Prediction
- GateNet:Gating-Enhanced Deep Network for Click-Through Rate Prediction
- Package Recommendation with Intra- and Inter-Package Attention Networks
十三、超分辨率分析SOTA模型（16个）
- Image Super-Resolution Using Deep Convolutional Networks
- Deeply-Recursive Convolutional Network for Image Super-Resolution
- Accelerating the Super-Resolution Convolutional Neural Network
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- Image super-resolution via deep recursive residual network
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- Image Super-Resolution Using Very Deep Residual Channel Attention Networks
- Image Super-Resolution via Dual-State Recurrent Networks
- Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
- Cascade Convolutional Neural Network for Image Super-Resolution
- Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
- Single Image Super-Resolution via a Holistic Attention Network
- One-to-many Approach for Improving Super-Resolution
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