Deep learning · generative models and autoencoders papers, 2023–2026
Deep learning area: generative_models_and_autoencoders
Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 4,314 papers are tagged with this topic.
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.
Papers per venue
Counts by year and venue
| Year | NeurIPS | ICLR | ICML | CVPR | Total |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
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 9 of 13
- ICML 2026PromptRL: Prompt Matters in RL for Flow-Based Image Generation
- ICML 2026Protein Fold Classification at Scale: Benchmarking and Pretraining
- ICML 2026Prototype-Grounded Concept Models for Verifiable Concept Alignment
- ICML 2026ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling
- ICML 2026Proximal-Based Generative Modeling for Bayesian Inverse Problems
- ICML 2026Pull Requests as a Training Signal for Repo-Level Code Editing
- ICML 2026PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
- ICML 2026Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
- ICML 2026Q-Tab: Quantized Tabular Data Generator
- ICML 2026Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
- ICML 2026Quantifying and Optimizing Simplicity via Polynomial Representations
- ICML 2026Quantifying Error Propagation and Model Collapse in Diffusion Models
- ICML 2026Quantum latent distributions in deep generative models
- ICML 2026Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
- ICML 2026RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
- ICML 2026RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
- ICML 2026RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
- ICML 2026Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
- ICML 2026Rare Event Analysis of Large Language Models
- ICML 2026Rationality Measurement and Theory for Reinforcement Learning Agents
- ICML 2026ReaForest: Fostering Generative Video Reasoning for Spatial Planning
- ICML 2026Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
- ICML 2026Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
- ICML 2026Reconstructing Template-Memorized Images from Natural Prompts
- ICML 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
- ICML 2026ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
- ICML 2026Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification
- ICML 2026RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
- ICML 2026Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
- ICML 2026Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
- ICML 2026Rényi Diffusion Models
- ICML 2026RePack then Refine: Efficient Diffusion Transformers with Vision Foundation Models
- ICML 2026Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation
- ICML 2026Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment
- ICML 2026ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
- ICML 2026Rethinking 3D Shape Generation: Diffusion over Superquadrics
- ICML 2026Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?
- ICML 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
- ICML 2026Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
- ICML 2026Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
- ICML 2026Rethinking Visual Intelligence: Insights from Video Pretraining
- ICML 2026RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
- ICML 2026Revisiting Spectral Representations in Generative Diffusion Models
- ICML 2026RGGT: A Generative-Prior-Guided Transformer for Unified Rigid and Non-Rigid Point Cloud Registration
- ICML 2026Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
- ICML 2026Riemannian MeanFlow
- ICML 2026Riemannian MeanFlow for One-Step Generation on Manifolds
- ICML 2026Riemannian Neural Optimal Transport
- ICML 2026RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
- ICML 2026RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
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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.
More in Deep Learning
- Diffusion Models 6,900
- Deep learning · large language models 2,334
- Deep learning · self supervised learning 1,958
- Deep learning · graph neural networks 1,648
- Deep learning · optimization 953
- Deep learning · representation learning 802
- Deep learning · probabilistic methods 637
- Deep learning · foundation models 536
- Deep learning · causal inference 316
- Deep learning · transfer learning 197