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

900 papers, page 14 of 18

651. ICML 2025VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
652. ICML 2025VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
653. ICML 2025Visual Generation Without Guidance
654. ICML 2025Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
655. ICML 2025WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models
656. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
657. ICML 2025WMAdapter: Adding WaterMark Control to Latent Diffusion Models
658. ICML 2025Wyckoff Transformer: Generation of Symmetric Crystals
659. ICML 2025Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
660. ICML 2025ZipAR: Parallel Autoregressive Image Generation through Spatial Locality
661. NeurIPS 20254D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time
662. NeurIPS 2025A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
663. NeurIPS 2025A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models
664. NeurIPS 2025A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
665. NeurIPS 2025A solvable model of learning generative diffusion: theory and insights
666. NeurIPS 2025Accelerating 3D Molecule Generative Models with Trajectory Diagnosis
667. NeurIPS 2025Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction
668. NeurIPS 2025Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
669. NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
670. NeurIPS 2025Alchemist: Turning Public Text-to-Image Data into Generative Gold
671. NeurIPS 2025Algorithm- and Data-Dependent Generalization Bounds for Diffusion Models
672. NeurIPS 2025All that structure matches does not glitter
673. NeurIPS 2025Ambient Diffusion Omni: Training Good Models with Bad Data
674. NeurIPS 2025AneuG-Flow: A Large-Scale Synthetic Dataset of Diverse Intracranial Aneurysm Geometries and Hemodynamics
675. NeurIPS 2025Approximation and Generalization Abilities of Score-based Neural Network Generative Models for Sub-Gaussian Distributions
676. NeurIPS 2025Assessing the quality of denoising diffusion models in Wasserstein distance: noisy score and optimal bounds
677. NeurIPS 2025Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
678. NeurIPS 2025AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and Fluid–Structure Interaction
679. NeurIPS 2025Balanced Conic Rectified Flow
680. NeurIPS 2025Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
681. NeurIPS 2025Beyond Scores: Proximal Diffusion Models
682. NeurIPS 2025BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints
683. NeurIPS 2025Bilevel Optimization for Adversarial Learning Problems: Sharpness, Generation, and Beyond
684. NeurIPS 2025BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction
685. NeurIPS 2025BitMark: Watermarking Bitwise Autoregressive Image Generative Models
686. NeurIPS 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
687. NeurIPS 2025Boosting Generative Image Modeling via Joint Image-Feature Synthesis
688. NeurIPS 2025BrainFlow: A Holistic Pathway of Dynamic Neural System on Manifold
689. NeurIPS 2025CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
690. NeurIPS 2025CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
691. NeurIPS 2025Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies
692. NeurIPS 2025CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis
693. NeurIPS 2025ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
694. NeurIPS 2025Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
695. NeurIPS 2025Composition and Alignment of Diffusion Models using Constrained Learning
696. NeurIPS 2025Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
697. NeurIPS 2025Compressed and Smooth Latent Space for Text Diffusion Modeling
698. NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
699. NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
700. NeurIPS 2025Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning

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.