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

1,430 papers, page 19 of 29

901. ICML 2026Reconstructing Template-Memorized Images from Natural Prompts
902. ICML 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
903. ICML 2026ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
904. ICML 2026Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification
905. ICML 2026RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
906. ICML 2026Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
907. ICML 2026Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
908. ICML 2026Rényi Diffusion Models
909. ICML 2026RePack then Refine: Efficient Diffusion Transformers with Vision Foundation Models
910. ICML 2026Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation
911. ICML 2026Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment
912. ICML 2026ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
913. ICML 2026Rethinking 3D Shape Generation: Diffusion over Superquadrics
914. ICML 2026Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?
915. ICML 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
916. ICML 2026Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
917. ICML 2026Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
918. ICML 2026Rethinking Visual Intelligence: Insights from Video Pretraining
919. ICML 2026RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
920. ICML 2026Revisiting Spectral Representations in Generative Diffusion Models
921. ICML 2026RGGT: A Generative-Prior-Guided Transformer for Unified Rigid and Non-Rigid Point Cloud Registration
922. ICML 2026Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
923. ICML 2026Riemannian MeanFlow
924. ICML 2026Riemannian MeanFlow for One-Step Generation on Manifolds
925. ICML 2026Riemannian Neural Optimal Transport
926. ICML 2026RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
927. ICML 2026RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
928. ICML 2026Robust and Consistent Ski Rental with Distributional Advice
929. ICML 2026Robust Cross-Modal Retrieval via Generative Semantic Refinement and Exclusion-Guided Adaptation
930. ICML 2026Robust Parallel Diffusion Sampling via Dynamic Jacobian Bandwidth
931. ICML 2026Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
932. ICML 2026RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
933. ICML 2026SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders
934. ICML 2026SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos
935. ICML 2026Scalable GANs with Transformers
936. ICML 2026Scalable RF Simulation in Generative 4D Worlds
937. ICML 2026Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
938. ICML 2026SCalDA: Semantics-Calibrated and Diffusion-Enhanced Data Augmentation
939. ICML 2026ScDiVa: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression
940. ICML 2026SceneDirector: Bridging Explicit Geometry and Generative Priors for Unified Driving Scene Editing
941. ICML 2026Score Based Error Correcting Code Decoder
942. ICML 2026Score Correction for Generative Models with Probabilistic Constraints
943. ICML 2026SDiD：Shared diffusion prior for efficient distributed stereo image compression
944. ICML 2026SE(3)-Equivariant Flow Matching with Gaussian Process Priors for Geometric Trajectory Prediction
945. ICML 2026SE(n)-Invariant Flow Matching: A General Framework with Application to Object Reassembly
946. ICML 2026Seeing the Unseen: Physics-as-Representation for Generalizable Gaze Perception
947. ICML 2026SegPVSG: Panoptic Video Scene Graph Generation via Temporal Focusing and Generative Augmentation
948. ICML 2026Self-Refining Video Sampling
949. ICML 2026Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
950. ICML 2026Semantic Editing with Coupled Stochastic Differential Equations

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.