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 1,304 篇，第 9 / 27 页

401. ICML 2026PromptRL: Prompt Matters in RL for Flow-Based Image Generation
402. ICML 2026Protein Fold Classification at Scale: Benchmarking and Pretraining
403. ICML 2026Prototype-Grounded Concept Models for Verifiable Concept Alignment
404. ICML 2026ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling
405. ICML 2026Proximal-Based Generative Modeling for Bayesian Inverse Problems
406. ICML 2026Pull Requests as a Training Signal for Repo-Level Code Editing
407. ICML 2026PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
408. ICML 2026Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
409. ICML 2026Q-Tab: Quantized Tabular Data Generator
410. ICML 2026Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
411. ICML 2026Quantifying and Optimizing Simplicity via Polynomial Representations
412. ICML 2026Quantifying Error Propagation and Model Collapse in Diffusion Models
413. ICML 2026Quantum latent distributions in deep generative models
414. ICML 2026Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
415. ICML 2026RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
416. ICML 2026RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
417. ICML 2026RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
418. ICML 2026Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
419. ICML 2026Rare Event Analysis of Large Language Models
420. ICML 2026Rationality Measurement and Theory for Reinforcement Learning Agents
421. ICML 2026ReaForest: Fostering Generative Video Reasoning for Spatial Planning
422. ICML 2026Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
423. ICML 2026Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
424. ICML 2026Reconstructing Template-Memorized Images from Natural Prompts
425. ICML 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
426. ICML 2026ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
427. ICML 2026Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification
428. ICML 2026RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
429. ICML 2026Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
430. ICML 2026Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
431. ICML 2026Rényi Diffusion Models
432. ICML 2026RePack then Refine: Efficient Diffusion Transformers with Vision Foundation Models
433. ICML 2026Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation
434. ICML 2026Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment
435. ICML 2026ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
436. ICML 2026Rethinking 3D Shape Generation: Diffusion over Superquadrics
437. ICML 2026Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?
438. ICML 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
439. ICML 2026Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
440. ICML 2026Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
441. ICML 2026Rethinking Visual Intelligence: Insights from Video Pretraining
442. ICML 2026RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
443. ICML 2026Revisiting Spectral Representations in Generative Diffusion Models
444. ICML 2026RGGT: A Generative-Prior-Guided Transformer for Unified Rigid and Non-Rigid Point Cloud Registration
445. ICML 2026Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
446. ICML 2026Riemannian MeanFlow
447. ICML 2026Riemannian MeanFlow for One-Step Generation on Manifolds
448. ICML 2026Riemannian Neural Optimal Transport
449. ICML 2026RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
450. ICML 2026RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization

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。