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

303 papers, page 3 of 7

101. ICLR 2026Frozen Priors, Fluid Forecasts: Prequential Uncertainty for Low-Data Deployment with Pretrained Generative Models
102. ICLR 2026FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models
103. ICLR 2026G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior
104. ICLR 2026GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
105. ICLR 2026GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL
106. ICLR 2026Gauge Flow Matching: Efficient Constrained Generative Modeling over General Convex Set and Beyond
107. ICLR 2026GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
108. ICLR 2026GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
109. ICLR 2026GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
110. ICLR 2026Gen-DFL: Decision-Focused Generative Learning for Robust Decision Making
111. ICLR 2026GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimation
112. ICLR 2026GenCompositor: Generative Video Compositing with Diffusion Transformer
113. ICLR 2026GenCtrl -- A Formal Controllability Toolkit for Generative Models
114. ICLR 2026GenDR: Lighten Generative Detail Restoration
115. ICLR 2026Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
116. ICLR 2026Generalization of Diffusion Models Arises with a Balanced Representation Space
117. ICLR 2026Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss
118. ICLR 2026Generative Adversarial Reasoner: Enhancing LLM Reasoning with Adversarial Reinforcement Learning
119. ICLR 2026Generative Bayesian Optimization: Generative Models as Acquisition Functions
120. ICLR 2026Generative Blocks World: Moving Things Around in Pictures
121. ICLR 2026Generative Human Geometry Distribution
122. ICLR 2026Generative Modeling from Black-Box Corruptions via Self-Consistent Stochastic Interpolants
123. ICLR 2026Generative Universal Verifier as Multimodal Meta-Reasoner
124. ICLR 2026Generative Value Conflicts Reveal LLM Priorities
125. ICLR 2026Generative View Stitching
126. ICLR 2026GenSR: Symbolic regression based on equation generative space
127. ICLR 2026Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
128. ICLR 2026GeomMotif: A Benchmark for Arbitrary Geometric Preservation in Protein Generation
129. ICLR 2026GGBall: Graph Generative Model on Poincaré Ball
130. ICLR 2026GoR: A Unified and Extensible Generative Framework for Ordinal Regression
131. ICLR 2026GRACE: Generative Representation Learning via Contrastive Policy Optimization
132. ICLR 2026Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models
133. ICLR 2026Grounding and Enhancing Informativeness and Utility in Dataset Distillation
134. ICLR 2026Guidance Watermarking for Diffusion Models
135. ICLR 2026H2OFlow: Grounding Human-Object Affordances with 3D Generative Models and Dense Diffused Flows
136. ICLR 2026Hierarchical Concept-based Interpretable Models
137. ICLR 2026High-dimensional Analysis of Synthetic Data Selection
138. ICLR 2026HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models
139. ICLR 2026HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series
140. ICLR 2026HoloPart: Generative 3D Part Amodal Segmentation
141. ICLR 2026How to Square Tensor Networks and Circuits Without Squaring Them
142. ICLR 2026Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
143. ICLR 2026Identifiability and recoverability in self-supervised models
144. ICLR 2026Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis
145. ICLR 2026Improving Black-Box Generative Attacks via Generator Semantic Consistency
146. ICLR 2026Improving Discrete Diffusion Unmasking Policies Beyond Explicit Reference Policies
147. ICLR 2026InfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement
148. ICLR 2026Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design
149. ICLR 2026InfoBridge: Mutual Information estimation via Bridge Matching
150. ICLR 2026Interaction Field Matching: Overcoming Limitations of Electrostatic Models

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.