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

303 papers, page 2 of 7

51. ICLR 2026DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
52. ICLR 2026DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights
53. ICLR 2026Delay Flow Matching
54. ICLR 2026DeMo: Decoupled Momentum Optimization
55. ICLR 2026Detecting and Mitigating Memorization in Diffusion Models through Anisotropy of the Log-Probability
56. ICLR 2026Diagnosing and Improving Diffusion Models by Estimating Optimal Loss Value
57. ICLR 2026DiCache: Let Diffusion Model Determine Its Own Cache
58. ICLR 2026DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
59. ICLR 2026Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
60. ICLR 2026Diffusion Language Model Knows the Answer Before It Decodes
61. ICLR 2026Diffusion Transformers with Representation Autoencoders
62. ICLR 2026Discovering and Steering Interpretable Concepts in Large Generative Music Models
63. ICLR 2026Disentanglement of Variations with Multimodal Generative Modeling
64. ICLR 2026Distributionally Robust Optimization via Generative Ambiguity Modeling
65. ICLR 2026Diversity-Aware Online Prompt Assignment to Generative Models
66. ICLR 2026Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
67. ICLR 2026Does Higher Interpretability Imply Better Utility? A Pairwise Analysis on Sparse Autoencoders
68. ICLR 2026DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
69. ICLR 2026dParallel: Learnable Parallel Decoding for dLLMs
70. ICLR 2026Dual-Solver: A Generalized ODE Solver for Diffusion Models with Dual Prediction
71. ICLR 2026DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
72. ICLR 2026EchoMotion: Unified Human Video and Motion Generation via Dual-Modality Diffusion Transformer
73. ICLR 2026EgoTwin: Dreaming Body and View in First Person
74. ICLR 2026Eliminating VAE for Fast and High-Resolution Generative Detail Restoration
75. ICLR 2026Energy-Based Transformers are Scalable Learners and Thinkers
76. ICLR 2026Enhanced Generative Model Evaluation with Clipped Density and Coverage
77. ICLR 2026Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
78. ICLR 2026Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport
79. ICLR 2026ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion
80. ICLR 2026Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence
81. ICLR 2026Evaluating GFlowNet from partial episodes for stable and flexible policy-based training
82. ICLR 2026Everything in Its Place: Benchmarking Spatial Intelligence of Text-to-Image Models
83. ICLR 2026FantasyWorld: Geometry-Consistent World Modeling via Unified Video and 3D Prediction
84. ICLR 2026FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning
85. ICLR 2026FAST‑DIPS: Adjoint‑Free Analytic Steps and Hard‑Constrained Likelihood Correction for Diffusion‑Prior Inverse Problems
86. ICLR 2026FastFlow: Accelerating The Generative Flow Matching Models with Bandit Inference
87. ICLR 2026FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation
88. ICLR 2026Finite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants
89. ICLR 2026FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion
90. ICLR 2026Flow Autoencoders are Effective Protein Tokenizers
91. ICLR 2026Flow Matching with Semidiscrete Couplings
92. ICLR 2026Flow of Spans: Generalizing Language Models to Dynamic Span-Vocabulary via GFlowNets
93. ICLR 2026Flow Straight and Fast in Hilbert Space: Functional Rectified Flow
94. ICLR 2026FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching
95. ICLR 2026FLOWER: A Flow-Matching Solver for Inverse Problems
96. ICLR 2026Foresight Diffusion: Improving Sampling Consistency in Predictive Diffusion Models
97. ICLR 2026Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models
98. ICLR 2026Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
99. ICLR 2026Frayed RoPE and Long Inputs: A Geometric Perspective
100. ICLR 2026Frequency-Balanced Retinal Representation Learning with Mutual Information Regularization

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.