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

174 papers, page 4 of 4

151. CVPR 2026SV-GS Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting
152. CVPR 2026Synthetic Object Compositions for Scalable and Accurate Learning in Detection
153. CVPR 2026TAG-MoE Task-Aware Gating for Unified Generative Mixture-of-Experts
154. CVPR 2026Taming Generative Diffusion Model for Task-Oriented Infrared Imaging
155. CVPR 2026Test-Time Instance-Specific Parameter Composition A New Paradigm for Adaptive Generative
156. CVPR 2026TGTrack Temporal Generative Learning for Unified Single Object Tracking
157. CVPR 2026Token Warping Helps MLLMs Look from Nearby Viewpoints
158. CVPR 2026Too Vivid to Be Real Benchmarking and Calibrating Generative Color
159. CVPR 2026Toward Diffusible High-Dimensional Latent Spaces A Frequency Perspective
160. CVPR 2026Towards Storytelling Animations Joint Synthesis of Human and Camera Motions
161. CVPR 2026Tracking by Predicting 3-D Gaussians Over Time
162. CVPR 2026Transition Models Rethinking the Generative Learning Objective
163. CVPR 2026TRIDENT A Trimodal Cascade Generative Framework for Drug and RNA-Conditioned
164. CVPR 2026Understanding Accelerating and Improving MeanFlow Training
165. CVPR 2026UniCorrn Unified Correspondence Transformer Across 2D and 3D
166. CVPR 2026Unified Latent Space for Understanding and Generation via Semantic Auto-encoder
167. CVPR 2026UniGenDet A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and
168. CVPR 2026UniTEX Universal High Fidelity Generative Texturing for 3D Shapes
169. CVPR 2026Velox Learning Representations of 4D Geometry and Appearance
170. CVPR 2026VideoMaMa Mask-Guided Video Matting via Generative Prior
171. CVPR 2026VOSR A Vision-Only Generative Model for Image Super-Resolution
172. CVPR 2026VQRAE Representation Quantization Autoencoders for Multimodal Understanding Generation and Reconstruction
173. CVPR 2026X-Part High Fidelity And Structure Coherent Shape Decomposition And Completion
174. CVPR 2026Your Classifier Can Do More Towards Balancing the Gaps in

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.