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

275 papers, page 6 of 6

251. ICML 2025Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
252. ICML 2025Training Diffusion-based Generative Models with Limited Data
253. ICML 2025Tree-Sliced Wasserstein Distance with Nonlinear Projection
254. ICML 2025Tree-Sliced Wasserstein Distance: A Geometric Perspective
255. ICML 2025Ultra-Resolution Adaptation with Ease
256. ICML 2025Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
257. ICML 2025Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
258. ICML 2025UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
259. ICML 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
260. ICML 2025Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
261. ICML 2025Variational Control for Guidance in Diffusion Models
262. ICML 2025Variational Learning of Fractional Posteriors
263. ICML 2025Variational Rectified Flow Matching
264. ICML 2025VCT: Training Consistency Models with Variational Noise Coupling
265. ICML 2025Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
266. ICML 2025VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
267. ICML 2025VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
268. ICML 2025Visual Generation Without Guidance
269. ICML 2025Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
270. ICML 2025WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models
271. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
272. ICML 2025WMAdapter: Adding WaterMark Control to Latent Diffusion Models
273. ICML 2025Wyckoff Transformer: Generation of Symmetric Crystals
274. ICML 2025Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
275. ICML 2025ZipAR: Parallel Autoregressive Image Generation through Spatial Locality

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.