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 1 of 5

1. NeurIPS 20254D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time
2. NeurIPS 2025A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
3. NeurIPS 2025A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models
4. NeurIPS 2025A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
5. NeurIPS 2025A solvable model of learning generative diffusion: theory and insights
6. NeurIPS 2025Accelerating 3D Molecule Generative Models with Trajectory Diagnosis
7. NeurIPS 2025Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction
8. NeurIPS 2025Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
9. NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
10. NeurIPS 2025Alchemist: Turning Public Text-to-Image Data into Generative Gold
11. NeurIPS 2025Algorithm- and Data-Dependent Generalization Bounds for Diffusion Models
12. NeurIPS 2025All that structure matches does not glitter
13. NeurIPS 2025Ambient Diffusion Omni: Training Good Models with Bad Data
14. NeurIPS 2025AneuG-Flow: A Large-Scale Synthetic Dataset of Diverse Intracranial Aneurysm Geometries and Hemodynamics
15. NeurIPS 2025Approximation and Generalization Abilities of Score-based Neural Network Generative Models for Sub-Gaussian Distributions
16. NeurIPS 2025Assessing the quality of denoising diffusion models in Wasserstein distance: noisy score and optimal bounds
17. NeurIPS 2025Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
18. NeurIPS 2025AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and Fluid–Structure Interaction
19. NeurIPS 2025Balanced Conic Rectified Flow
20. NeurIPS 2025Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
21. NeurIPS 2025Beyond Scores: Proximal Diffusion Models
22. NeurIPS 2025BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints
23. NeurIPS 2025Bilevel Optimization for Adversarial Learning Problems: Sharpness, Generation, and Beyond
24. NeurIPS 2025BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction
25. NeurIPS 2025BitMark: Watermarking Bitwise Autoregressive Image Generative Models
26. NeurIPS 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
27. NeurIPS 2025Boosting Generative Image Modeling via Joint Image-Feature Synthesis
28. NeurIPS 2025BrainFlow: A Holistic Pathway of Dynamic Neural System on Manifold
29. NeurIPS 2025CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
30. NeurIPS 2025CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
31. NeurIPS 2025Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies
32. NeurIPS 2025CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis
33. NeurIPS 2025ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
34. NeurIPS 2025Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
35. NeurIPS 2025Composition and Alignment of Diffusion Models using Constrained Learning
36. NeurIPS 2025Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
37. NeurIPS 2025Compressed and Smooth Latent Space for Text Diffusion Modeling
38. NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
39. NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
40. NeurIPS 2025Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning
41. NeurIPS 2025Continuity and Isolation Lead to Doubts or Dilemmas in Large Language Models
42. NeurIPS 2025Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
43. NeurIPS 2025Controllable Human-centric Keyframe Interpolation with Generative Prior
44. NeurIPS 2025CORAL: Disentangling Latent Representations in Long-Tailed Diffusion
45. NeurIPS 2025Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
46. NeurIPS 2025Counterfactual Identifiability via Dynamic Optimal Transport
47. NeurIPS 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridge
48. NeurIPS 2025Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training
49. NeurIPS 2025DeblurDiff: Real-Word Image Deblurring with Generative Diffusion Models
50. NeurIPS 2025DeCaFlow: A deconfounding causal generative model

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.