Deep learning · generative models and autoencoders papers, 2023–2026

Key findings

• Deep learning · generative models and autoencoders went from 1,216 papers in 2023 to 1,430 in 2026, roughly 1.2x in three years.
• NeurIPS publishes the most Deep learning · generative models and autoencoders work: 1,714 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Image Generation: papers tagged with both rose from 35 in 2023 to 150 in 2026.
• Among the 11 topics under Deep Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 1.2x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Deep learning · generative models and autoencoders papers

240 papers, page 4 of 5

151. NeurIPS 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation
152. NeurIPS 2025Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
153. NeurIPS 2025PID-controlled Langevin Dynamics for Faster Sampling on Generative Models
154. NeurIPS 2025Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
155. NeurIPS 2025Plug-and-Play Context Feature Reuse for Efficient Masked Generation
156. NeurIPS 2025Position: Benchmarking is Broken - Don't Let AI be Its Own Judge
157. NeurIPS 2025Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism
158. NeurIPS 2025Preconditioned Langevin Dynamics with Score-based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
159. NeurIPS 2025Principled Long-Tailed Generative Modeling via Diffusion Models
160. NeurIPS 2025Probing Equivariance and Symmetry Breaking in Convolutional Networks
161. NeurIPS 2025Prohibiting Generative AI in any Form of Weapon Control
162. NeurIPS 2025Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
163. NeurIPS 2025Random Forest Autoencoders for Guided Representation Learning
164. NeurIPS 2025Real-Time Hyper-Personalized Generative AI Should Be Regulated to Prevent the Rise of "Digital Heroin"
165. NeurIPS 2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
166. NeurIPS 2025ReDi: Rectified Discrete Flow
167. NeurIPS 2025REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training
168. NeurIPS 2025RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
169. NeurIPS 2025Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws
170. NeurIPS 2025RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching
171. NeurIPS 2025RGB-to-Polarization Estimation: A New Task and Benchmark Study
172. NeurIPS 2025Riemannian Consistency Model
173. NeurIPS 2025Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
174. NeurIPS 2025Robust and Scalable Autonomous Reinforcement Learning in Irreversible Environments
175. NeurIPS 2025RODS: Robust Optimization Inspired Diffusion Sampling for Detecting and Reducing Hallucination in Generative Models
176. NeurIPS 2025ROGR: Relightable 3D Objects using Generative Relighting
177. NeurIPS 2025Rotary Masked Autoencoders are Versatile Learners
178. NeurIPS 2025SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders
179. NeurIPS 2025Salient Concept-Aware Generative Data Augmentation
180. NeurIPS 2025Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
181. NeurIPS 2025Sample-Efficient Multi-Round Generative Data Augmentation for Long-Tail Instance Segmentation
182. NeurIPS 2025Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
183. NeurIPS 2025Scalable In-context Ranking with Generative Models
184. NeurIPS 2025Scaling Offline RL via Efficient and Expressive Shortcut Models
185. NeurIPS 2025Schrödinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres
186. NeurIPS 2025SCoT: Unifying Consistency Models and Rectified Flows via Straight-Consistent Trajectories
187. NeurIPS 2025Seeds of Structure: Patch PCA Reveals Universal Compositional Cues in Diffusion Models
188. NeurIPS 2025Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
189. NeurIPS 2025SGAR: Structural Generative Augmentation for 3D Human Motion Retrieval
190. NeurIPS 2025Shape-Informed Clustering of Multi-Dimensional Functional Data via Deep Functional Autoencoders
191. NeurIPS 2025ShapeEmbed: a self-supervised learning framework for 2D contour quantification
192. NeurIPS 2025Shortcut Features as Top Eigenfunctions of NTK: A Linear Neural Network Case and More
193. NeurIPS 2025Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression
194. NeurIPS 2025Smooth and Flexible Camera Movement Synthesis via Temporal Masked Generative Modeling
195. NeurIPS 2025SOMBRL: Scalable and Optimistic Model-Based RL
196. NeurIPS 2025Space Group Equivariant Crystal Diffusion
197. NeurIPS 2025SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
198. NeurIPS 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
199. NeurIPS 2025SpecEM: Training-Free LLM Ensembling via Iterative Drafting, Verification, and Online Feedback
200. NeurIPS 2025Spectral Conditioning of Attention Improves Transformer Performance

FAQ

How many Deep learning · generative models and autoencoders papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

4,314 in total: 1,216 in 2023, 768 in 2024, 900 in 2025, 1,430 in 2026.

How many Deep learning · generative models and autoencoders papers are at NeurIPS 2026?

NeurIPS 2026 has 349 Deep learning · generative models and autoencoders papers, versus 240 the year before.

How many Deep learning · generative models and autoencoders papers are at ICLR 2026?

ICLR 2026 has 303 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at ICML 2026?

ICML 2026 has 604 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at CVPR 2026?

CVPR 2026 has 174 Deep learning · generative models and autoencoders papers, versus 110 the year before.

Which conference publishes the most Deep learning · generative models and autoencoders papers?

NeurIPS, with 1,714 papers from 2023 to 2026.