深度学习 · generative models and autoencoders论文趋势 2023–2026

主要结论

• 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
• NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部深度学习 · generative models and autoencoders论文

共 4,314 篇，第 27 / 87 页

1301. NeurIPS 2026Phase Kernel Lifts Capacity of Dense Associative Memory
1302. NeurIPS 2026PhaseDance: Capturing Rhythm and Expressivity in Dance Modeling
1303. NeurIPS 2026PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
1304. NeurIPS 2026Plan4D: Generative Plannable 4D Worlds
1305. NeurIPS 2026Plug-and-Play ADMM for Inverse Problems with Flow Matching Denoiser
1306. NeurIPS 2026Point-to-Manifold Geometry: Flexibly Overcoming the Curse of Dimensionality in Neural Computational Units
1307. NeurIPS 2026Position: We Need Greater Transparency to Maintain Research Pipeline Reliability Despite GenAI
1308. NeurIPS 2026Positional Encoding and Prompt-Order Sensitivity in In-Context Learning
1309. NeurIPS 2026PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers
1310. NeurIPS 2026PP-Mark: Provable and Publicly Verifiable Watermarking for Generative AI
1311. NeurIPS 2026Preconditioned Flow Matching
1312. NeurIPS 2026Predict-Project-Renoise: Sampling Diffusion Models under Hard Constraints
1313. NeurIPS 2026Predicting directional flexibility in proteins
1314. NeurIPS 2026Prevailing Bisimulation Metric Learning Is Biased: Implicit Regularization and Its Remedy
1315. NeurIPS 2026PRISM:Disentangling Preference Distributions for Generative Ranking
1316. NeurIPS 2026Probability-Conserving Flow Guidance
1317. NeurIPS 2026ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
1318. NeurIPS 2026Projection Learning: A Principled Way to Overcome Memorization in Distribution Learning
1319. NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
1320. NeurIPS 2026RA-CFGCache: From Branch-Level Criteria to Guided-Risk Control under Classifier-Free Guidance
1321. NeurIPS 2026RA-ClipScore: Making Generative Model Evaluation More Interpretable
1322. NeurIPS 2026RadarMAE: Injecting Physical Inductive Biases into Masked Autoencoders for Advanced Radar Object Detection
1323. NeurIPS 2026Rate-Constrained Edge Metadata for Sender–Receiver Generative Video Super-Resolution
1324. NeurIPS 2026Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
1325. NeurIPS 2026Reasoning with Sampling: Cutting at Decision Points
1326. NeurIPS 2026Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
1327. NeurIPS 2026Reflected Schrödinger Bridge Matching
1328. NeurIPS 2026Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow Models
1329. NeurIPS 2026Relaxation-Aligned State Control for Test-Time Scaling in Generative Combinatorial Optimization
1330. NeurIPS 2026Renoise Consistency: Unlocking Efficient Self-Correction for Diffusion Large Language Models
1331. NeurIPS 2026Rethinking Knowledge Distillation for Diffusion Language Models
1332. NeurIPS 2026Rethinking Memorization–Generalization Trade-Off in Generative Models
1333. NeurIPS 2026Rethinking SAE Evaluation: An Atomic Interpretable Unit Framework Reveals Hidden Polysemanticity and Redundancy
1334. NeurIPS 2026Rethinking Semantic ID Construction for Generative Recommendation: SimHash with Parallel Decoding and Semantic Alignment
1335. NeurIPS 2026Rethinking Vector Field Learning for Generative Segmentation
1336. NeurIPS 2026Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review
1337. NeurIPS 2026Revisiting The Power of Closed-Form: Robust Deep Image Prototype Discovery via Scale Mixtures
1338. NeurIPS 2026Revisiting Value Iteration: Unified Analysis of Discounted and Average-Reward Cases
1339. NeurIPS 2026Reward Is Not a Universal Interface for Generative Reinforcement Learning
1340. NeurIPS 2026Reweighted Flow Matching via Unbalanced Optimal Transport for Label-free Long-tailed Generation
1341. NeurIPS 2026RGF: Recursive Generative Framework for the Edge-Cut Separable Problems
1342. NeurIPS 2026Robust Flow Matching under Target Corruption and Label Noise
1343. NeurIPS 2026Rotations on Latent Hyperspheres: a Geometry-Aware Guiding Framework for Diffusion Models
1344. NeurIPS 2026Routing as a Singular Reparameterization: A Closed-Form Pushforward Prior and Exact Bayesian Complexity in a Minimal Proxy
1345. NeurIPS 2026Safety Reconstructed: Generative Modeling via Masked Diffusion Builds Strong Safety Guardrails
1346. NeurIPS 2026SAGE: Semantically Disentangled Representation Learning through Latent Geometry Constraint and Large Language Model
1347. NeurIPS 2026Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders
1348. NeurIPS 2026Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
1349. NeurIPS 2026Sample-Efficient Optimization over Generative Priors via Coarse Learnability
1350. NeurIPS 2026SARA: Step-Adaptive Rank Adjustment for Diffusion Inference Acceleration

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？

共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。

NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？

NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。

ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？

ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。

CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？

CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。

哪个会议的深度学习 · generative models and autoencoders论文最多？

NeurIPS，2023–2026 年共 1,714 篇。