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

604 papers, page 12 of 13

551. ICML 2026Trajectory-Aware Spiking DiTs Conversion via Membrane Potential Error-Feedback
552. ICML 2026TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
553. ICML 2026Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
554. ICML 2026Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
555. ICML 2026TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
556. ICML 2026Twins: Learn to Predict Unified Representations with Focal Loss
557. ICML 2026Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
558. ICML 2026Two-Parameter Flows for Learning Population Dynamics of Physical Systems
559. ICML 2026UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
560. ICML 2026Uncovering Competency Gaps in Large Language Models and Their Benchmarks
561. ICML 2026Understanding LoRA as Knowledge Memory: An Empirical Analysis
562. ICML 2026Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective
563. ICML 2026Understanding Transfer Learning of RNA Foundation Models on Downstream Tasks
564. ICML 2026Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
565. ICML 2026UnHype: CLIP-Guided Hypernetworks for Dynamic LoRA Unlearning
566. ICML 2026UniFLoW: Universal Multi-Modal Federated LoRA Fine-Tuning Framework with Analytical Aggregation
567. ICML 2026Unifying Masked Diffusion Models with Various Generation Orders and Beyond
568. ICML 2026Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
569. ICML 2026Unpaired Visual Editing with Self-Consistent Flow Matching
570. ICML 2026Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
571. ICML 2026Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization
572. ICML 2026Value-as-Return: A Two-Stage Framework to Align on the Optimal Score Function
573. ICML 2026VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation
574. ICML 2026Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture – Bridging Predictive and Generative Self-Supervised Learning
575. ICML 2026Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
576. ICML 2026Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments
577. ICML 2026Variational Bayesian Flow Network for Graph Generation
578. ICML 2026Variational Entropic Optimal Transport
579. ICML 2026Variational Flow Maps: Make Some Noise for One-Step Conditional Generation
580. ICML 2026VecDesigner: Exploring Visual Guidance and Structural Consistency for Semantic Typography
581. ICML 2026VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphs
582. ICML 2026VELR: Efficient Video Reward Feedback via Ensemble Latent Reward Models
583. ICML 2026VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization
584. ICML 2026VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation
585. ICML 2026VideoMAETok: Boosting Video Diffusion Models via Masked Autoencoders as Tokenizers
586. ICML 2026Visual Implicit Autoregressive Modeling
587. ICML 2026ViTok-v2: Scaling Native Resolution Autoencoders to 5 Billion Parameters
588. ICML 2026Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
589. ICML 2026Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
590. ICML 2026When Diffusion Language Models Hesitate: Detecting and Correcting Visual Hallucinations via Confidence Fluctuation
591. ICML 2026When Do Diffusion Models Learn to Generate Multiple Objects?
592. ICML 2026When Model Merging Breaks Routing: Training-Free Calibration for MoE
593. ICML 2026When RL Meets Adaptive Speculative Training: A Unified Training-Serving System
594. ICML 2026When Sample Selection Bias Precipitates Model Collapse
595. ICML 2026Where Concept Erasure Should Occur: Concept–Layer Alignment in Text-to-Video Diffusion Models
596. ICML 2026Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
597. ICML 2026Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
598. ICML 2026WhisperSplat: Lossless Steganography in 3D Gaussian Splatting
599. ICML 2026Who Said Neural Networks Aren't Linear?
600. ICML 2026Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics

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.