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

349 papers, page 7 of 7

301. NeurIPS 2024Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
302. NeurIPS 2024Taming Generative Diffusion Prior for Universal Blind Image Restoration
303. NeurIPS 2024Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
304. NeurIPS 2024Tetrahedron Splatting for 3D Generation
305. NeurIPS 2024The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
306. NeurIPS 2024The GAN is dead; long live the GAN! A Modern GAN Baseline
307. NeurIPS 2024The tree autoencoder model, with application to hierarchical data visualization
308. NeurIPS 2024Theoretical guarantees in KL for Diffusion Flow Matching
309. NeurIPS 2024Thought of Search: Planning with Language Models Through The Lens of Efficiency
310. NeurIPS 2024Time-Reversal Provides Unsupervised Feedback to LLMs
311. NeurIPS 2024Towards a Scalable Reference-Free Evaluation of Generative Models
312. NeurIPS 2024Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
313. NeurIPS 2024Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
314. NeurIPS 2024Training Data Attribution via Approximate Unrolling
315. NeurIPS 2024TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks
316. NeurIPS 2024Transcendence: Generative Models Can Outperform The Experts That Train Them
317. NeurIPS 2024Transcoders find interpretable LLM feature circuits
318. NeurIPS 2024Transferring disentangled representations: bridging the gap between synthetic and real images
319. NeurIPS 2024Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
320. NeurIPS 2024Transformer Doctor: Diagnosing and Treating Vision Transformers
321. NeurIPS 2024Transformers Can Do Arithmetic with the Right Embeddings
322. NeurIPS 2024Transformers on Markov data: Constant depth suffices
323. NeurIPS 2024Truncated Variance Reduced Value Iteration
324. NeurIPS 2024TSDS: Data Selection for Task-Specific Model Finetuning
325. NeurIPS 2024UDPM: Upsampling Diffusion Probabilistic Models
326. NeurIPS 2024Understanding Hallucinations in Diffusion Models through Mode Interpolation
327. NeurIPS 2024Understanding Information Storage and Transfer in Multi-Modal Large Language Models
328. NeurIPS 2024UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
329. NeurIPS 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
330. NeurIPS 2024Unity by Diversity: Improved Representation Learning for Multimodal VAEs
331. NeurIPS 2024Universal In-Context Approximation By Prompting Fully Recurrent Models
332. NeurIPS 2024Universal Sample Coding
333. NeurIPS 2024Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
334. NeurIPS 2024Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance
335. NeurIPS 2024Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
336. NeurIPS 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
337. NeurIPS 2024VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
338. NeurIPS 2024Video Diffusion Models are Training-free Motion Interpreter and Controller
339. NeurIPS 2024VideoTetris: Towards Compositional Text-to-Video Generation
340. NeurIPS 2024Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels
341. NeurIPS 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
342. NeurIPS 2024Visual Data Diagnosis and Debiasing with Concept Graphs
343. NeurIPS 2024Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion
344. NeurIPS 2024Visual Perception by Large Language Model’s Weights
345. NeurIPS 2024Vivid-ZOO: Multi-View Video Generation with Diffusion Model
346. NeurIPS 2024WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models
347. NeurIPS 2024Wings: Learning Multimodal LLMs without Text-only Forgetting
348. NeurIPS 2024YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals
349. NeurIPS 2024ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification

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.