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

768 papers, page 3 of 16

101. CVPR 2024VBench: Comprehensive Benchmark Suite for Video Generative Models
102. CVPR 2024VideoMAC: Video Masked Autoencoders Meet ConvNets
103. CVPR 2024Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
104. CVPR 2024Wired Perspectives: Multi-View Wire Art Embraces Generative AI
105. CVPR 2024Would Deep Generative Models Amplify Bias in Future Models?
106. CVPR 2024XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
107. ICLR 2024$\alpha$TC-VAE: On the relationship between Disentanglement and Diversity
108. ICLR 2024$\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
109. ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
110. ICLR 2024A Flexible Generative Model for Heterogeneous Tabular EHR with Missing Modality
111. ICLR 2024A Simple and Scalable Representation for Graph Generation
112. ICLR 2024A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
113. ICLR 2024A Variational Perspective on Solving Inverse Problems with Diffusion Models
114. ICLR 2024AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
115. ICLR 2024AlpaGasus: Training a Better Alpaca with Fewer Data
116. ICLR 2024Amortizing intractable inference in large language models
117. ICLR 2024Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity
118. ICLR 2024Analyzing and Improving Optimal-Transport-based Adversarial Networks
119. ICLR 2024Are Bert Family Good Instruction Followers? A Study on Their Potential And Limitations
120. ICLR 2024Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
121. ICLR 2024BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
122. ICLR 2024Beating Price of Anarchy and Gradient Descent without Regret in Potential Games
123. ICLR 2024Bellman Optimal Stepsize Straightening of Flow-Matching Models
124. ICLR 2024Bespoke Solvers for Generative Flow Models
125. ICLR 2024Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
126. ICLR 2024Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
127. ICLR 2024CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
128. ICLR 2024Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
129. ICLR 2024Compositional Generative Inverse Design
130. ICLR 2024Concept Bottleneck Generative Models
131. ICLR 2024Conditional Variational Diffusion Models
132. ICLR 2024Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
133. ICLR 2024Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach
134. ICLR 2024Course Correcting Koopman Representations
135. ICLR 2024DDMI: Domain-agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
136. ICLR 2024Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
137. ICLR 2024Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders
138. ICLR 2024Delta-AI: Local objectives for amortized inference in sparse graphical models
139. ICLR 2024Demystifying Embedding Spaces using Large Language Models
140. ICLR 2024DENEVIL: TOWARDS DECIPHERING AND NAVIGATING THE ETHICAL VALUES OF LARGE LANGUAGE MODELS VIA INSTRUCTION LEARNING
141. ICLR 2024Denoising Diffusion Bridge Models
142. ICLR 2024Denoising Diffusion Step-aware Models
143. ICLR 2024Detecting, Explaining, and Mitigating Memorization in Diffusion Models
144. ICLR 2024Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking
145. ICLR 2024Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
146. ICLR 2024DiffusionSat: A Generative Foundation Model for Satellite Imagery
147. ICLR 2024DistillSpec: Improving Speculative Decoding via Knowledge Distillation
148. ICLR 2024Divide and not forget: Ensemble of selectively trained experts in Continual Learning
149. ICLR 2024Don't Play Favorites: Minority Guidance for Diffusion Models
150. ICLR 2024DreamLLM: Synergistic Multimodal Comprehension and Creation

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.