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

776 papers, page 4 of 16

151. NeurIPS 2023Counterfactual Generation with Identifiability Guarantees
152. NeurIPS 2023Counterfactually Fair Representation
153. NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
154. NeurIPS 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
155. NeurIPS 2023CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography
156. NeurIPS 2023CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation
157. NeurIPS 2023CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
158. NeurIPS 2023Curvature Filtrations for Graph Generative Model Evaluation
159. NeurIPS 2023Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
160. NeurIPS 2023DAC-DETR: Divide the Attention Layers and Conquer
161. NeurIPS 2023Data Quality in Imitation Learning
162. NeurIPS 2023Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation
163. NeurIPS 2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
164. NeurIPS 2023Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
165. NeurIPS 2023Debiased and Denoised Entity Recognition from Distant Supervision
166. NeurIPS 2023Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes
167. NeurIPS 2023Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
168. NeurIPS 2023Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
169. NeurIPS 2023Deep Fractional Fourier Transform
170. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
171. NeurIPS 2023Deep Insights into Noisy Pseudo Labeling on Graph Data
172. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
173. NeurIPS 2023Deep Non-line-of-sight Imaging from Under-scanning Measurements
174. NeurIPS 2023Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
175. NeurIPS 2023Deep Patch Visual Odometry
176. NeurIPS 2023Deep Recurrent Optimal Stopping
177. NeurIPS 2023Deep Stochastic Processes via Functional Markov Transition Operators
178. NeurIPS 2023Delegated Classification
179. NeurIPS 2023Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
180. NeurIPS 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
181. NeurIPS 2023Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
182. NeurIPS 2023DiffComplete: Diffusion-based Generative 3D Shape Completion
183. NeurIPS 2023Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes
184. NeurIPS 2023Differentiable Random Partition Models
185. NeurIPS 2023Differentially Private Image Classification by Learning Priors from Random Processes
186. NeurIPS 2023DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
187. NeurIPS 2023DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
188. NeurIPS 2023Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence
189. NeurIPS 2023Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
190. NeurIPS 2023Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
191. NeurIPS 2023Diffusion Schrödinger Bridge Matching
192. NeurIPS 2023Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
193. NeurIPS 2023Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback
194. NeurIPS 2023Direct Diffusion Bridge using Data Consistency for Inverse Problems
195. NeurIPS 2023Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
196. NeurIPS 2023Disentanglement via Latent Quantization
197. NeurIPS 2023Disentangling Voice and Content with Self-Supervision for Speaker Recognition
198. NeurIPS 2023Distributed Inference and Fine-tuning of Large Language Models Over The Internet
199. NeurIPS 2023Distributed Personalized Empirical Risk Minimization
200. NeurIPS 2023Distribution Learnability and Robustness

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.