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 17 of 18

801. NeurIPS 2025Non-Markovian Discrete Diffusion with Causal Language Models
802. NeurIPS 2025On scalable and efficient training of diffusion samplers
803. NeurIPS 2025On the Relation between Rectified Flows and Optimal Transport
804. NeurIPS 2025One-Step Diffusion-Based Image Compression with Semantic Distillation
805. NeurIPS 2025Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization
806. NeurIPS 2025Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation
807. NeurIPS 2025Orientation Matters: Making 3D Generative Models Orientation-Aligned
808. NeurIPS 2025PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
809. NeurIPS 2025PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling
810. NeurIPS 2025PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
811. NeurIPS 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation
812. NeurIPS 2025Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
813. NeurIPS 2025PID-controlled Langevin Dynamics for Faster Sampling on Generative Models
814. NeurIPS 2025Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
815. NeurIPS 2025Plug-and-Play Context Feature Reuse for Efficient Masked Generation
816. NeurIPS 2025Position: Benchmarking is Broken - Don't Let AI be Its Own Judge
817. NeurIPS 2025Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism
818. NeurIPS 2025Preconditioned Langevin Dynamics with Score-based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
819. NeurIPS 2025Principled Long-Tailed Generative Modeling via Diffusion Models
820. NeurIPS 2025Probing Equivariance and Symmetry Breaking in Convolutional Networks
821. NeurIPS 2025Prohibiting Generative AI in any Form of Weapon Control
822. NeurIPS 2025Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
823. NeurIPS 2025Random Forest Autoencoders for Guided Representation Learning
824. NeurIPS 2025Real-Time Hyper-Personalized Generative AI Should Be Regulated to Prevent the Rise of "Digital Heroin"
825. NeurIPS 2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
826. NeurIPS 2025ReDi: Rectified Discrete Flow
827. NeurIPS 2025REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training
828. NeurIPS 2025RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
829. NeurIPS 2025Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws
830. NeurIPS 2025RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching
831. NeurIPS 2025RGB-to-Polarization Estimation: A New Task and Benchmark Study
832. NeurIPS 2025Riemannian Consistency Model
833. NeurIPS 2025Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
834. NeurIPS 2025Robust and Scalable Autonomous Reinforcement Learning in Irreversible Environments
835. NeurIPS 2025RODS: Robust Optimization Inspired Diffusion Sampling for Detecting and Reducing Hallucination in Generative Models
836. NeurIPS 2025ROGR: Relightable 3D Objects using Generative Relighting
837. NeurIPS 2025Rotary Masked Autoencoders are Versatile Learners
838. NeurIPS 2025SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders
839. NeurIPS 2025Salient Concept-Aware Generative Data Augmentation
840. NeurIPS 2025Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
841. NeurIPS 2025Sample-Efficient Multi-Round Generative Data Augmentation for Long-Tail Instance Segmentation
842. NeurIPS 2025Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
843. NeurIPS 2025Scalable In-context Ranking with Generative Models
844. NeurIPS 2025Scaling Offline RL via Efficient and Expressive Shortcut Models
845. NeurIPS 2025Schrödinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres
846. NeurIPS 2025SCoT: Unifying Consistency Models and Rectified Flows via Straight-Consistent Trajectories
847. NeurIPS 2025Seeds of Structure: Patch PCA Reveals Universal Compositional Cues in Diffusion Models
848. NeurIPS 2025Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
849. NeurIPS 2025SGAR: Structural Generative Augmentation for 3D Human Motion Retrieval
850. NeurIPS 2025Shape-Informed Clustering of Multi-Dimensional Functional Data via Deep Functional Autoencoders

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.