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

78 papers, page 1 of 2

1. ICLR 2023A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
2. ICLR 2023A Self-Attention Ansatz for Ab-initio Quantum Chemistry
3. ICLR 2023AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection
4. ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
5. ICLR 2023Auto-Encoding Goodness of Fit
6. ICLR 2023Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
7. ICLR 2023Better Generative Replay for Continual Federated Learning
8. ICLR 2023Blurring Diffusion Models
9. ICLR 2023Boosting Multiagent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks
10. ICLR 2023Building Normalizing Flows with Stochastic Interpolants
11. ICLR 2023CFlowNets: Continuous Control with Generative Flow Networks
12. ICLR 2023ChiroDiff: Modelling chirographic data with Diffusion Models
13. ICLR 2023Continual Unsupervised Disentangling of Self-Organizing Representations
14. ICLR 2023DAVA: Disentangling Adversarial Variational Autoencoder
15. ICLR 2023DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
16. ICLR 2023Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
17. ICLR 2023Deep Generative Symbolic Regression
18. ICLR 2023Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
19. ICLR 2023Denoising Masked Autoencoders Help Robust Classification
20. ICLR 2023Deterministic training of generative autoencoders using invertible layers
21. ICLR 2023Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
22. ICLR 2023DiffusER: Diffusion via Edit-based Reconstruction
23. ICLR 2023Diffusion Probabilistic Fields
24. ICLR 2023Diffusion-GAN: Training GANs with Diffusion
25. ICLR 2023Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
26. ICLR 2023Explicitly Minimizing the Blur Error of Variational Autoencoders
27. ICLR 2023Fast Sampling of Diffusion Models with Exponential Integrator
28. ICLR 2023Finding the Global Semantic Representation in GAN through Fréchet Mean
29. ICLR 2023Flow Matching for Generative Modeling
30. ICLR 2023Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
31. ICLR 2023Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions
32. ICLR 2023Generative Augmented Flow Networks
33. ICLR 2023Generative Modeling Helps Weak Supervision (and Vice Versa)
34. ICLR 2023Generative Modelling with Inverse Heat Dissipation
35. ICLR 2023Geometrically regularized autoencoders for non-Euclidean data
36. ICLR 2023GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
37. ICLR 2023GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure
38. ICLR 2023Gromov-Wasserstein Autoencoders
39. ICLR 2023Guiding Energy-based Models via Contrastive Latent Variables
40. ICLR 2023Hierarchical Sliced Wasserstein Distance
41. ICLR 2023ImaginaryNet: Learning Object Detectors without Real Images and Annotations
42. ICLR 2023Imitating Human Behaviour with Diffusion Models
43. ICLR 2023InCoder: A Generative Model for Code Infilling and Synthesis
44. ICLR 2023Incremental Learning of Structured Memory via Closed-Loop Transcription
45. ICLR 2023Learning Diffusion Bridges on Constrained Domains
46. ICLR 2023MeshDiffusion: Score-based Generative 3D Mesh Modeling
47. ICLR 2023MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises
48. ICLR 2023Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
49. ICLR 2023Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
50. ICLR 2023Neural Implicit Shape Editing using Boundary Sensitivity

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.