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

1,216 papers, page 15 of 25

701. NeurIPS 2023FABind: Fast and Accurate Protein-Ligand Binding
702. NeurIPS 2023Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
703. NeurIPS 2023FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
704. NeurIPS 2023Facing Off World Model Backbones: RNNs, Transformers, and S4
705. NeurIPS 2023Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
706. NeurIPS 2023Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
707. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
708. NeurIPS 2023Fast Attention Requires Bounded Entries
709. NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
710. NeurIPS 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
711. NeurIPS 2023Fast Trainable Projection for Robust Fine-tuning
712. NeurIPS 2023Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
713. NeurIPS 2023Feature Learning for Interpretable, Performant Decision Trees
714. NeurIPS 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
715. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
716. NeurIPS 2023Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
717. NeurIPS 2023Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
718. NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation
719. NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
720. NeurIPS 2023Fine-Tuning Language Models with Just Forward Passes
721. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
722. NeurIPS 2023First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
723. NeurIPS 2023Flow Factorized Representation Learning
724. NeurIPS 2023Flow: Per-instance Personalized Federated Learning
725. NeurIPS 2023Formulating Discrete Probability Flow Through Optimal Transport
726. NeurIPS 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
727. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
728. NeurIPS 2023Functional Renyi Differential Privacy for Generative Modeling
729. NeurIPS 2023Fundamental Limits and Tradeoffs in Invariant Representation Learning
730. NeurIPS 2023Game Solving with Online Fine-Tuning
731. NeurIPS 2023Gaussian Mixture Solvers for Diffusion Models
732. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
733. NeurIPS 2023Generalized Information-theoretic Multi-view Clustering
734. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
735. NeurIPS 2023Generalizing Nonlinear ICA Beyond Structural Sparsity
736. NeurIPS 2023Generating QM1B with PySCF$_{\text{IPU}}$
737. NeurIPS 2023Generative Category-level Object Pose Estimation via Diffusion Models
738. NeurIPS 2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
739. NeurIPS 2023Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
740. NeurIPS 2023Generative Neural Fields by Mixtures of Neural Implicit Functions
741. NeurIPS 2023Generator Born from Classifier
742. NeurIPS 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
743. NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
744. NeurIPS 2023Geometry-Aware Adaptation for Pretrained Models
745. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
746. NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
747. NeurIPS 2023Global Optimality in Bivariate Gradient-based DAG Learning
748. NeurIPS 2023Globally injective and bijective neural operators
749. NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
750. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks

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.