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 1,430 篇，第 18 / 29 页

851. ICML 2026Partial Ring Scan: Revisiting Scan Order in Vision State Space Models
852. ICML 2026Particle-Guided Diffusion Models for Partial Differential Equations
853. ICML 2026Periodic Bayesian Flow Networks with Additive Accuracy
854. ICML 2026Personalized Image Generation via Human-in-the-loop Bayesian Optimization
855. ICML 2026Phase-Type Variational Autoencoders for Heavy-Tailed Data
856. ICML 2026Physics-Guided Motion Loss for Video Generation Model
857. ICML 2026Physics-Informed Diffusion Models in Spectral Space
858. ICML 2026PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards
859. ICML 2026Plan, Decouple, Assimilate: Physics-Aware Object Insertion in Remote Sensing Imagery
860. ICML 2026Planar Symmetric Pattern Generation
861. ICML 2026Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction
862. ICML 2026PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
863. ICML 2026POLCA: Stochastic Generative Optimization with LLM
864. ICML 2026PolyFlow: Safe and Efficient Polytope-Constrained Flow Matching with Constraint Embedding and Projection-free Update
865. ICML 2026Polyphonia: Zero-Shot Timbre Transfer in Polyphonic Music with Acoustic-Informed Attention Calibration
866. ICML 2026PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding
867. ICML 2026PortraitRL: Reinforcement Learning for Personalized Portrait Pose Transfer with Multi-Objective Reward Modeling
868. ICML 2026Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
869. ICML 2026Primal-Spectral Generative Modeling: Fast Analytical Generation via Pseudoinverse Lévy Inversion
870. ICML 2026Principled Confidence Estimation for Deep Computed Tomography
871. ICML 2026Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization
872. ICML 2026Prism-MoE: Efficient Dense-to-MoE Conversion for Visual Autoregressive Generation
873. ICML 2026PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
874. ICML 2026Probing the Geometry of Diffusion Models with the String Method
875. ICML 2026Problem Distributions as Tasks: Repurposing Meta Learning for Generative Combinatorial Optimization towards Multi-task Pretraining and Adaptation
876. ICML 2026ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
877. ICML 2026Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers
878. ICML 2026PromptRL: Prompt Matters in RL for Flow-Based Image Generation
879. ICML 2026Protein Fold Classification at Scale: Benchmarking and Pretraining
880. ICML 2026Prototype-Grounded Concept Models for Verifiable Concept Alignment
881. ICML 2026ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling
882. ICML 2026Proximal-Based Generative Modeling for Bayesian Inverse Problems
883. ICML 2026Pull Requests as a Training Signal for Repo-Level Code Editing
884. ICML 2026PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
885. ICML 2026Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
886. ICML 2026Q-Tab: Quantized Tabular Data Generator
887. ICML 2026Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
888. ICML 2026Quantifying and Optimizing Simplicity via Polynomial Representations
889. ICML 2026Quantifying Error Propagation and Model Collapse in Diffusion Models
890. ICML 2026Quantum latent distributions in deep generative models
891. ICML 2026Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
892. ICML 2026RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
893. ICML 2026RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
894. ICML 2026RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
895. ICML 2026Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
896. ICML 2026Rare Event Analysis of Large Language Models
897. ICML 2026Rationality Measurement and Theory for Reinforcement Learning Agents
898. ICML 2026ReaForest: Fostering Generative Video Reasoning for Spatial Planning
899. ICML 2026Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
900. ICML 2026Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning

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。