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
303 papers, page 5 of 7
- ICLR 2026Parallel Sampling from Masked Diffusion Models via Conditional Independence Testing
- ICLR 2026Partition Generative Modeling: Masked Modeling Without Masks
- ICLR 2026Pixel-Perfect Puppetry: Precision-Guided Enhancement for Face Image and Video Editing
- ICLR 2026PixNerd: Pixel Neural Field Diffusion
- ICLR 2026Plug-and-Play Fidelity Optimization for Diffusion Transformer Acceleration via Cumulative Error Minimization
- ICLR 2026PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives
- ICLR 2026Practical estimation of the optimal classification error with soft labels and calibration
- ICLR 2026PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generation
- ICLR 2026Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift
- ICLR 2026Pretraining Scaling Laws for Generative Evaluations of Language Models
- ICLR 2026Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
- ICLR 2026Priors in time: Missing inductive biases for language model interpretability
- ICLR 2026Product-Quantised Image Representation for High-Quality Image Synthesis
- ICLR 2026PROTDYN: A FOUNDATION PROTEIN LANGUAGE MODEL FOR THERMODYNAMICS AND DYNAMICS GENERATION
- ICLR 2026ProteinAE: Protein Diffusion Autoencoders for Structure Encoding
- ICLR 2026Provable Separations between Memorization and Generalization in Diffusion Models
- ICLR 2026Purifying Generative LLMs from Backdoors without Prior Knowledge or Clean Reference
- ICLR 2026Quasi-Monte Carlo Methods Enable Extremely Low-Dimensional Deep Generative Models
- ICLR 2026Quotient-Space Diffusion Model
- ICLR 2026ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing
- ICLR 2026Riemannian Variational Flow Matching for Material and Protein Design
- ICLR 2026RNE: plug-and-play diffusion inference-time control and energy-based training
- ICLR 2026SAFER: Risk-Constrained Sample-then-Filter in Large Language Models
- ICLR 2026Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
- ICLR 2026Scaling Group Inference for Diverse and High-Quality Generation
- ICLR 2026Scaling Sequence-to-Sequence Generative Neural Rendering
- ICLR 2026Scaling Speech Tokenizers with Diffusion Autoencoders
- ICLR 2026ScalingCache: Extreme Acceleration of DiTs through Difference Scaling and Dynamic Interval Caching
- ICLR 2026SCOPED: Score–Curvature Out-of-distribution Proximity Evaluator for Diffusion
- ICLR 2026Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
- ICLR 2026Self-Speculative Decoding Accelerates Lossless Inference in Any-Order and Any-Subset Autoregressive Models
- ICLR 2026Self-Speculative Masked Diffusions
- ICLR 2026SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image Generation
- ICLR 2026SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
- ICLR 2026SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
- ICLR 2026Shift-and-Sum Quantization for Visual Autoregressive Models
- ICLR 2026Shrinking Proteins with Diffusion
- ICLR 2026SIGMA-GEN: STRUCTURE AND IDENTITY GUIDED MULTI-SUBJECT ASSEMBLY FOR IMAGE GENERATION
- ICLR 2026Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
- ICLR 2026SimpleFold: Folding Proteins is Simpler than You Think
- ICLR 2026SimpleGVR: A Simple Baseline for Latent-Cascaded Generative Video Super-Resolution
- ICLR 2026SoFlow: Solution Flow Models for One-Step Generative Modeling
- ICLR 2026SONA: Learning Conditional, Unconditional, and Matching-Aware Discriminator
- ICLR 2026SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
- ICLR 2026Sparse Autoencoders Trained on the Same Data Learn Different Features
- ICLR 2026Sparse CLIP: Co-Optimizing Interpretability and Performance in Contrastive Learning
- ICLR 2026SparseD: Sparse Attention for Diffusion Language Models
- ICLR 2026SpatialHand: Generative Object Manipulation from 3D Prespective
- ICLR 2026Spatially Informed Autoencoders for Interpretable Visual Representation Learning
- ICLR 2026SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism
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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