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

768 papers, page 5 of 16

201. ICLR 2024Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling
202. ICLR 2024Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
203. ICLR 2024Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments
204. ICLR 2024Lifting Architectural Constraints of Injective Flows
205. ICLR 2024Light Schrödinger Bridge
206. ICLR 2024Lipschitz Singularities in Diffusion Models
207. ICLR 2024LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses
208. ICLR 2024Localizing and Editing Knowledge In Text-to-Image Generative Models
209. ICLR 2024Long-tailed Diffusion Models with Oriented Calibration
210. ICLR 2024LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
211. ICLR 2024Machine Unlearning for Image-to-Image Generative Models
212. ICLR 2024Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
213. ICLR 2024Manifold Diffusion Fields
214. ICLR 2024Manifold Preserving Guided Diffusion
215. ICLR 2024Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
216. ICLR 2024MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods
217. ICLR 2024MiniLLM: Knowledge Distillation of Large Language Models
218. ICLR 2024Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
219. ICLR 2024Modelling complex vector drawings with stroke-clouds
220. ICLR 2024Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
221. ICLR 2024Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
222. ICLR 2024Multimarginal Generative Modeling with Stochastic Interpolants
223. ICLR 2024Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
224. ICLR 2024NEFTune: Noisy Embeddings Improve Instruction Finetuning
225. ICLR 2024Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
226. ICLR 2024Neural Optimal Transport with General Cost Functionals
227. ICLR 2024Neural structure learning with stochastic differential equations
228. ICLR 2024Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
229. ICLR 2024Noise-free Score Distillation
230. ICLR 2024OctoPack: Instruction Tuning Code Large Language Models
231. ICLR 2024On Error Propagation of Diffusion Models
232. ICLR 2024On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models
233. ICLR 2024On the Foundations of Shortcut Learning
234. ICLR 2024On the Reliability of Watermarks for Large Language Models
235. ICLR 2024On the Stability of Iterative Retraining of Generative Models on their own Data
236. ICLR 2024PhyloGFN: Phylogenetic inference with generative flow networks
237. ICLR 2024Pre-Training and Fine-Tuning Generative Flow Networks
238. ICLR 2024Provable Compositional Generalization for Object-Centric Learning
239. ICLR 2024Quality-Diversity through AI Feedback
240. ICLR 2024R-MAE: Regions Meet Masked Autoencoders
241. ICLR 2024Raidar: geneRative AI Detection viA Rewriting
242. ICLR 2024Relay Diffusion: Unifying diffusion process across resolutions for image synthesis
243. ICLR 2024Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
244. ICLR 2024Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
245. ICLR 2024SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
246. ICLR 2024Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization
247. ICLR 2024SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
248. ICLR 2024Scale-Adaptive Diffusion Model for Complex Sketch Synthesis
249. ICLR 2024Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions
250. ICLR 2024SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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.