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

4,314 papers, page 57 of 87

2801. NeurIPS 2024Consistency of Neural Causal Partial Identification
2802. NeurIPS 2024Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
2803. NeurIPS 2024Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT
2804. NeurIPS 2024ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models
2805. NeurIPS 2024COSMIC: Compress Satellite Image Efficiently via Diffusion Compensation
2806. NeurIPS 2024CRAG - Comprehensive RAG Benchmark
2807. NeurIPS 2024Credit Attribution and Stable Compression
2808. NeurIPS 2024CryoBench: Datasets and Benchmarks for Heterogeneous Cryo-EM Reconstruction
2809. NeurIPS 2024CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy
2810. NeurIPS 2024Cryptographic Hardness of Score Estimation
2811. NeurIPS 2024Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
2812. NeurIPS 2024CV-VAE: A Compatible Video VAE for Latent Generative Video Models
2813. NeurIPS 2024DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain
2814. NeurIPS 2024Data Attribution for Text-to-Image Models by Unlearning Synthesized Images
2815. NeurIPS 2024Debiasing Synthetic Data Generated by Deep Generative Models
2816. NeurIPS 2024DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection
2817. NeurIPS 2024DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
2818. NeurIPS 2024DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform
2819. NeurIPS 2024DenoiseRep: Denoising Model for Representation Learning
2820. NeurIPS 2024DiffPhyCon: A Generative Approach to Control Complex Physical Systems
2821. NeurIPS 2024Diffusion Models With Learned Adaptive Noise
2822. NeurIPS 2024Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies
2823. NeurIPS 2024Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models
2824. NeurIPS 2024DiffusionPDE: Generative PDE-Solving under Partial Observation
2825. NeurIPS 2024DiMSUM: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation
2826. NeurIPS 2024DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization
2827. NeurIPS 2024Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer
2828. NeurIPS 2024Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
2829. NeurIPS 2024Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
2830. NeurIPS 2024Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models
2831. NeurIPS 2024DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation
2832. NeurIPS 2024DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models
2833. NeurIPS 2024Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images
2834. NeurIPS 2024Dynamic Conditional Optimal Transport through Simulation-Free Flows
2835. NeurIPS 2024Dynamic Service Fee Pricing under Strategic Behavior: Actions as Instruments and Phase Transition
2836. NeurIPS 2024EM Distillation for One-step Diffusion Models
2837. NeurIPS 2024Embedding-Aligned Language Models
2838. NeurIPS 2024ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis
2839. NeurIPS 2024Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
2840. NeurIPS 2024Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
2841. NeurIPS 2024Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination
2842. NeurIPS 2024EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models
2843. NeurIPS 2024Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
2844. NeurIPS 2024Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention
2845. NeurIPS 2024Estimating the Hallucination Rate of Generative AI
2846. NeurIPS 2024Evaluating the design space of diffusion-based generative models
2847. NeurIPS 2024Evaluating the World Model Implicit in a Generative Model
2848. NeurIPS 2024Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
2849. NeurIPS 2024Exocentric-to-Egocentric Video Generation
2850. NeurIPS 2024Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models

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.