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 17 of 35

801. NeurIPS 2024MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing
802. NeurIPS 2024MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
803. NeurIPS 2024Nature-Inspired Local Propagation
804. NeurIPS 2024Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
805. NeurIPS 2024Nearest Neighbor Speculative Decoding for LLM Generation and Attribution
806. NeurIPS 2024Neural Cover Selection for Image Steganography
807. NeurIPS 2024Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
808. NeurIPS 2024Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses
809. NeurIPS 2024OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation
810. NeurIPS 2024On Divergence Measures for Training GFlowNets
811. NeurIPS 2024On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
812. NeurIPS 2024On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
813. NeurIPS 2024On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
814. NeurIPS 2024On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
815. NeurIPS 2024On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
816. NeurIPS 2024On the Noise Robustness of In-Context Learning for Text Generation
817. NeurIPS 2024One-Shot Safety Alignment for Large Language Models via Optimal Dualization
818. NeurIPS 2024Optimal Flow Matching: Learning Straight Trajectories in Just One Step
819. NeurIPS 2024Optimal Multiclass U-Calibration Error and Beyond
820. NeurIPS 2024Order-Independence Without Fine Tuning
821. NeurIPS 2024PaCE: Parsimonious Concept Engineering for Large Language Models
822. NeurIPS 2024PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher
823. NeurIPS 2024Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
824. NeurIPS 2024Parametric model reduction of mean-field and stochastic systems via higher-order action matching
825. NeurIPS 2024Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
826. NeurIPS 2024PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision
827. NeurIPS 2024PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
828. NeurIPS 2024PERIA: Perceive, Reason, Imagine, Act via Holistic Language and Vision Planning for Manipulation
829. NeurIPS 2024Pessimistic Backward Policy for GFlowNets
830. NeurIPS 2024Phased Consistency Models
831. NeurIPS 2024PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging
832. NeurIPS 2024Piecewise deterministic generative models
833. NeurIPS 2024Poisson Variational Autoencoder
834. NeurIPS 2024Polyhedral Complex Derivation from Piecewise Trilinear Networks
835. NeurIPS 2024Practical Shuffle Coding
836. NeurIPS 2024Pricing and Competition for Generative AI
837. NeurIPS 2024ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing
838. NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
839. NeurIPS 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
840. NeurIPS 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
841. NeurIPS 2024PTQ4DiT: Post-training Quantization for Diffusion Transformers
842. NeurIPS 2024PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
843. NeurIPS 2024QGFN: Controllable Greediness with Action Values
844. NeurIPS 2024QTIP: Quantization with Trellises and Incoherence Processing
845. NeurIPS 2024Questioning the Survey Responses of Large Language Models
846. NeurIPS 2024Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
847. NeurIPS 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
848. NeurIPS 2024Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
849. NeurIPS 2024RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
850. NeurIPS 2024Rethinking Score Distillation as a Bridge Between Image Distributions

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.