深度学习 · generative models and autoencoders论文趋势 2023–2026

主要结论

• 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
• NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
• ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
• ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
• CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
• NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
• ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
• ICLR 2025A Watermark for Order-Agnostic Language Models
• CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Fixed-Point Approach for Causal Generative Modeling
• ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
• ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
• CVPR 20233D GAN Inversion With Facial Symmetry Prior
• NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
• ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
• ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
• CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back

全部深度学习 · generative models and autoencoders论文

共 174 篇，第 3 / 4 页

101. ICLR 2024LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses
102. ICLR 2024Localizing and Editing Knowledge In Text-to-Image Generative Models
103. ICLR 2024Long-tailed Diffusion Models with Oriented Calibration
104. ICLR 2024LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
105. ICLR 2024Machine Unlearning for Image-to-Image Generative Models
106. ICLR 2024Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
107. ICLR 2024Manifold Diffusion Fields
108. ICLR 2024Manifold Preserving Guided Diffusion
109. ICLR 2024Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
110. ICLR 2024MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods
111. ICLR 2024MiniLLM: Knowledge Distillation of Large Language Models
112. ICLR 2024Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
113. ICLR 2024Modelling complex vector drawings with stroke-clouds
114. ICLR 2024Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
115. ICLR 2024Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
116. ICLR 2024Multimarginal Generative Modeling with Stochastic Interpolants
117. ICLR 2024Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
118. ICLR 2024NEFTune: Noisy Embeddings Improve Instruction Finetuning
119. ICLR 2024Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
120. ICLR 2024Neural Optimal Transport with General Cost Functionals
121. ICLR 2024Neural structure learning with stochastic differential equations
122. ICLR 2024Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
123. ICLR 2024Noise-free Score Distillation
124. ICLR 2024OctoPack: Instruction Tuning Code Large Language Models
125. ICLR 2024On Error Propagation of Diffusion Models
126. ICLR 2024On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models
127. ICLR 2024On the Foundations of Shortcut Learning
128. ICLR 2024On the Reliability of Watermarks for Large Language Models
129. ICLR 2024On the Stability of Iterative Retraining of Generative Models on their own Data
130. ICLR 2024PhyloGFN: Phylogenetic inference with generative flow networks
131. ICLR 2024Pre-Training and Fine-Tuning Generative Flow Networks
132. ICLR 2024Provable Compositional Generalization for Object-Centric Learning
133. ICLR 2024Quality-Diversity through AI Feedback
134. ICLR 2024R-MAE: Regions Meet Masked Autoencoders
135. ICLR 2024Raidar: geneRative AI Detection viA Rewriting
136. ICLR 2024Relay Diffusion: Unifying diffusion process across resolutions for image synthesis
137. ICLR 2024Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
138. ICLR 2024Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
139. ICLR 2024SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
140. ICLR 2024Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization
141. ICLR 2024SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
142. ICLR 2024Scale-Adaptive Diffusion Model for Complex Sketch Synthesis
143. ICLR 2024Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions
144. ICLR 2024SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
145. ICLR 2024SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
146. ICLR 2024Self-Alignment with Instruction Backtranslation
147. ICLR 2024Self-Consuming Generative Models Go MAD
148. ICLR 2024Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
149. ICLR 2024Separate and Diffuse: Using a Pretrained Diffusion Model for Better Source Separation
150. ICLR 2024SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？

共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。

NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？

NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。

ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？

CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。

哪个会议的深度学习 · generative models and autoencoders论文最多？

NeurIPS，2023–2026 年共 1,714 篇。