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 34 of 35

1651. NeurIPS 2023Topological Parallax: A Geometric Specification for Deep Perception Models
1652. NeurIPS 2023TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
1653. NeurIPS 2023Toward Understanding Generative Data Augmentation
1654. NeurIPS 2023Towards a Unified Framework of Contrastive Learning for Disentangled Representations
1655. NeurIPS 2023Towards Automated Circuit Discovery for Mechanistic Interpretability
1656. NeurIPS 2023Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly
1657. NeurIPS 2023Towards Free Data Selection with General-Purpose Models
1658. NeurIPS 2023Towards robust and generalizable representations of extracellular data using contrastive learning
1659. NeurIPS 2023Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
1660. NeurIPS 2023Towards Test-Time Refusals via Concept Negation
1661. NeurIPS 2023Towards Unbounded Machine Unlearning
1662. NeurIPS 2023Trade-off Between Efficiency and Consistency for Removal-based Explanations
1663. NeurIPS 2023Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
1664. NeurIPS 2023Training Energy-Based Normalizing Flow with Score-Matching Objectives
1665. NeurIPS 2023Training Private Models That Know What They Don’t Know
1666. NeurIPS 2023Training Transitive and Commutative Multimodal Transformers with LoReTTa
1667. NeurIPS 2023Trans-Dimensional Generative Modeling via Jump Diffusion Models
1668. NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
1669. NeurIPS 2023Transformer-based Planning for Symbolic Regression
1670. NeurIPS 2023Tree Variational Autoencoders
1671. NeurIPS 2023Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
1672. NeurIPS 2023Truly Scale-Equivariant Deep Nets with Fourier Layers
1673. NeurIPS 2023TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
1674. NeurIPS 2023Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
1675. NeurIPS 2023Unbiased learning of deep generative models with structured discrete representations
1676. NeurIPS 2023Uncertainty Quantification via Neural Posterior Principal Components
1677. NeurIPS 2023Uncertainty-Aware Instance Reweighting for Off-Policy Learning
1678. NeurIPS 2023Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
1679. NeurIPS 2023Understanding and Improving Feature Learning for Out-of-Distribution Generalization
1680. NeurIPS 2023Understanding and Mitigating Copying in Diffusion Models
1681. NeurIPS 2023Understanding Contrastive Learning via Distributionally Robust Optimization
1682. NeurIPS 2023Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes
1683. NeurIPS 2023Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
1684. NeurIPS 2023Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
1685. NeurIPS 2023Undirected Probabilistic Model for Tensor Decomposition
1686. NeurIPS 2023Unifying GANs and Score-Based Diffusion as Generative Particle Models
1687. NeurIPS 2023Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model
1688. NeurIPS 2023UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
1689. NeurIPS 2023Unsupervised Image Denoising with Score Function
1690. NeurIPS 2023Use perturbations when learning from explanations
1691. NeurIPS 2023VanillaNet: the Power of Minimalism in Deep Learning
1692. NeurIPS 2023Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
1693. NeurIPS 2023VeriX: Towards Verified Explainability of Deep Neural Networks
1694. NeurIPS 2023Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
1695. NeurIPS 2023Video Prediction Models as Rewards for Reinforcement Learning
1696. NeurIPS 2023VisAlign: Dataset for Measuring the Alignment between AI and Humans in Visual Perception
1697. NeurIPS 2023Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
1698. NeurIPS 2023Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
1699. NeurIPS 2023What Do Deep Saliency Models Learn about Visual Attention?
1700. NeurIPS 2023What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization 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.