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

1,216 papers, page 2 of 25

51. CVPR 2023Overcoming the Trade-Off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
52. CVPR 2023Persistent Nature: A Generative Model of Unbounded 3D Worlds
53. CVPR 2023PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
54. CVPR 2023Reliable and Interpretable Personalized Federated Learning
55. CVPR 2023Restoration of Hand-Drawn Architectural Drawings Using Latent Space Mapping With Degradation Generator
56. CVPR 2023Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens
57. CVPR 2023Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution
58. CVPR 2023RGBD2: Generative Scene Synthesis via Incremental View Inpainting Using RGBD Diffusion Models
59. CVPR 2023RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
60. CVPR 2023SceneTrilogy: On Human Scene-Sketch and Its Complementarity With Photo and Text
61. CVPR 2023Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
62. CVPR 2023Seeing a Rose in Five Thousand Ways
63. CVPR 2023ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations
64. CVPR 2023SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene
65. CVPR 2023SMAE: Few-Shot Learning for HDR Deghosting With Saturation-Aware Masked Autoencoders
66. CVPR 2023Spider GAN: Leveraging Friendly Neighbors To Accelerate GAN Training
67. CVPR 2023The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
68. CVPR 2023Towards End-to-End Generative Modeling of Long Videos With Memory-Efficient Bidirectional Transformers
69. CVPR 2023Towards Practical Plug-and-Play Diffusion Models
70. CVPR 2023Towards Transferable Targeted Adversarial Examples
71. CVPR 2023Towards Universal Fake Image Detectors That Generalize Across Generative Models
72. CVPR 2023Understanding Deep Generative Models With Generalized Empirical Likelihoods
73. CVPR 2023Understanding Masked Autoencoders via Hierarchical Latent Variable Models
74. CVPR 2023VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking
75. CVPR 2023Visual Prompt Tuning for Generative Transfer Learning
76. CVPR 2023Zero-Shot Model Diagnosis
77. ICLR 2023A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
78. ICLR 2023A Self-Attention Ansatz for Ab-initio Quantum Chemistry
79. ICLR 2023AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection
80. ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
81. ICLR 2023Auto-Encoding Goodness of Fit
82. ICLR 2023Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
83. ICLR 2023Better Generative Replay for Continual Federated Learning
84. ICLR 2023Blurring Diffusion Models
85. ICLR 2023Boosting Multiagent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks
86. ICLR 2023Building Normalizing Flows with Stochastic Interpolants
87. ICLR 2023CFlowNets: Continuous Control with Generative Flow Networks
88. ICLR 2023ChiroDiff: Modelling chirographic data with Diffusion Models
89. ICLR 2023Continual Unsupervised Disentangling of Self-Organizing Representations
90. ICLR 2023DAVA: Disentangling Adversarial Variational Autoencoder
91. ICLR 2023DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
92. ICLR 2023Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
93. ICLR 2023Deep Generative Symbolic Regression
94. ICLR 2023Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
95. ICLR 2023Denoising Masked Autoencoders Help Robust Classification
96. ICLR 2023Deterministic training of generative autoencoders using invertible layers
97. ICLR 2023Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
98. ICLR 2023DiffusER: Diffusion via Edit-based Reconstruction
99. ICLR 2023Diffusion Probabilistic Fields
100. ICLR 2023Diffusion-GAN: Training GANs with Diffusion

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.