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

604 papers, page 8 of 13

351. ICML 2026OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
352. ICML 2026OmniShow: Unifying Multimodal Conditions for Human-Object Interaction Video Generation
353. ICML 2026OMP: One-step Meanflow Policy with Directional Alignment
354. ICML 2026On the Anisotropy of Score-Based Generative Models
355. ICML 2026On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering
356. ICML 2026On the Fragility of Data Attribution When Learning Is Distributed
357. ICML 2026On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching
358. ICML 2026On the Relationship Between Activation Outliers and Feature Death in Sparse Autoencoders
359. ICML 2026On the Separability of Information in Diffusion Models
360. ICML 2026One-step Latent-free Image Generation with Pixel Mean Flows
361. ICML 2026One-step Optimal Transport via Regularized Distribution Matching Distillation
362. ICML 2026One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation
363. ICML 2026OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
364. ICML 2026Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining
365. ICML 2026Optimal Stopping in Latent Diffusion Models
366. ICML 2026Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech
367. ICML 2026Optimizing Few-Step Generation with Adaptive Matching Distillation
368. ICML 2026Optimizing Visual Generative Models via Distribution-wise Rewards
369. ICML 2026Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
370. ICML 2026Overclocking Electrostatic Generative Models
371. ICML 2026OVLR: Efficient, Scalable, and Robust Training via Output-Level Variance-Reduced Likelihood Ratio
372. ICML 2026PADS-TAL: Padding-Annealed Diffusion Sampling in Text-Aware Latent Space for Robust and Diverse Text-to-Music Generation
373. ICML 2026Parameter Manifold Purification
374. ICML 2026Partial Ring Scan: Revisiting Scan Order in Vision State Space Models
375. ICML 2026Particle-Guided Diffusion Models for Partial Differential Equations
376. ICML 2026Periodic Bayesian Flow Networks with Additive Accuracy
377. ICML 2026Personalized Image Generation via Human-in-the-loop Bayesian Optimization
378. ICML 2026Phase-Type Variational Autoencoders for Heavy-Tailed Data
379. ICML 2026Physics-Guided Motion Loss for Video Generation Model
380. ICML 2026Physics-Informed Diffusion Models in Spectral Space
381. ICML 2026PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards
382. ICML 2026Plan, Decouple, Assimilate: Physics-Aware Object Insertion in Remote Sensing Imagery
383. ICML 2026Planar Symmetric Pattern Generation
384. ICML 2026Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction
385. ICML 2026PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
386. ICML 2026POLCA: Stochastic Generative Optimization with LLM
387. ICML 2026PolyFlow: Safe and Efficient Polytope-Constrained Flow Matching with Constraint Embedding and Projection-free Update
388. ICML 2026Polyphonia: Zero-Shot Timbre Transfer in Polyphonic Music with Acoustic-Informed Attention Calibration
389. ICML 2026PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding
390. ICML 2026PortraitRL: Reinforcement Learning for Personalized Portrait Pose Transfer with Multi-Objective Reward Modeling
391. ICML 2026Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
392. ICML 2026Primal-Spectral Generative Modeling: Fast Analytical Generation via Pseudoinverse Lévy Inversion
393. ICML 2026Principled Confidence Estimation for Deep Computed Tomography
394. ICML 2026Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization
395. ICML 2026Prism-MoE: Efficient Dense-to-MoE Conversion for Visual Autoregressive Generation
396. ICML 2026PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
397. ICML 2026Probing the Geometry of Diffusion Models with the String Method
398. ICML 2026Problem Distributions as Tasks: Repurposing Meta Learning for Generative Combinatorial Optimization towards Multi-task Pretraining and Adaptation
399. ICML 2026ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
400. ICML 2026Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers

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.