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

1,714 papers, page 12 of 35

551. NeurIPS 2025Steering Generative Models with Experimental Data for Protein Fitness Optimization
552. NeurIPS 2025StegoZip: Enhancing Linguistic Steganography Payload in Practice with Large Language Models
553. NeurIPS 2025StruDiCO: Structured Denoising Diffusion with Gradient-free Inference-stage Boosting for Memory and Time Efficient Combinatorial Optimization
554. NeurIPS 2025SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction
555. NeurIPS 2025System-Embedded Diffusion Bridge Models
556. NeurIPS 2025Taming generative video models for zero-shot optical flow extraction
557. NeurIPS 2025TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE
558. NeurIPS 2025TC-Light: Temporally Coherent Generative Rendering for Realistic World Transfer
559. NeurIPS 2025The Generative Leap: Tight Sample Complexity for Efficiently Learning Gaussian Multi-Index Models
560. NeurIPS 2025The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
561. NeurIPS 2025ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation
562. NeurIPS 2025Towards a Pairwise Ranking Model with Orderliness and Monotonicity for Label Enhancement
563. NeurIPS 2025Towards A Translative Model of Sperm Whale Vocalization
564. NeurIPS 2025Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution
565. NeurIPS 2025Training-Free Safe Denoisers for Safe Use of Diffusion Models
566. NeurIPS 2025Transferring Linear Features Across Language Models With Model Stitching
567. NeurIPS 2025Transformer Key-Value Memories Are Nearly as Interpretable as Sparse Autoencoders
568. NeurIPS 2025Transformers for Mixed-type Event Sequences
569. NeurIPS 2025Transition Matching: Scalable and Flexible Generative Modeling
570. NeurIPS 2025TreeGen: A Bayesian Generative Model for Hierarchies
571. NeurIPS 2025UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis
572. NeurIPS 2025un$^2$CLIP: Improving CLIP's Visual Detail Capturing Ability via Inverting unCLIP
573. NeurIPS 2025Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
574. NeurIPS 2025Universal Video Temporal Grounding with Generative Multi-modal Large Language Models
575. NeurIPS 2025Unveiling Concept Attribution in Diffusion Models
576. NeurIPS 2025UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation
577. NeurIPS 2025Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision
578. NeurIPS 2025Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
579. NeurIPS 2025VisDiff: SDF-Guided Polygon Generation for Visibility Reconstruction, Characterization and Recognition
580. NeurIPS 2025Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting
581. NeurIPS 2025Wasserstein Convergence of Critically Damped Langevin Diffusions
582. NeurIPS 2025What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
583. NeurIPS 2025When and how can inexact generative models still sample from the data manifold?
584. NeurIPS 2025When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective
585. NeurIPS 2025When Models Don’t Collapse: On the Consistency of Iterative MLE
586. NeurIPS 2025Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models
587. NeurIPS 2025Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
588. NeurIPS 2025Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
589. NeurIPS 2025WritingBench: A Comprehensive Benchmark for Generative Writing
590. NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
591. NeurIPS 20244Diffusion: Multi-view Video Diffusion Model for 4D Generation
592. NeurIPS 20244M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
593. NeurIPS 20244Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models
594. NeurIPS 2024A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding Optimization
595. NeurIPS 2024A Closer Look at AUROC and AUPRC under Class Imbalance
596. NeurIPS 2024A Functional Extension of Semi-Structured Networks
597. NeurIPS 2024A Generative Model of Symmetry Transformations
598. NeurIPS 2024A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
599. NeurIPS 2024A scalable generative model for dynamical system reconstruction from neuroimaging data
600. NeurIPS 2024A Simple and Optimal Approach for Universal Online Learning with Gradient Variations

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.