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整理了197个经典SOTA模型，涵盖图像分类、目标检测、推荐系统等13个方向

news 2025/9/14 16:12:20

今天来帮大家回顾一下计算机视觉、自然语言处理等热门研究领域的197个经典SOTA模型，涵盖了图像分类、图像生成、文本分类、强化学习、目标检测、推荐系统、语音识别等13个细分方向。建议大家收藏了慢慢看，下一篇顶会的idea这就来了~

由于整理的SOTA模型有点多，这里只做简单分享，全部论文以及项目源码看文末

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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