深度学习 · generative models and autoencoders论文趋势 2023–2026
ICML/NeurIPS 深度学习方向:generative_models_and_autoencoders
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 4,314 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
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 240 篇,第 1 / 5 页
- NeurIPS 20254D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time
- NeurIPS 2025A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
- NeurIPS 2025A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models
- NeurIPS 2025A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
- NeurIPS 2025A solvable model of learning generative diffusion: theory and insights
- NeurIPS 2025Accelerating 3D Molecule Generative Models with Trajectory Diagnosis
- NeurIPS 2025Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction
- NeurIPS 2025Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
- NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
- NeurIPS 2025Alchemist: Turning Public Text-to-Image Data into Generative Gold
- NeurIPS 2025Algorithm- and Data-Dependent Generalization Bounds for Diffusion Models
- NeurIPS 2025All that structure matches does not glitter
- NeurIPS 2025Ambient Diffusion Omni: Training Good Models with Bad Data
- NeurIPS 2025AneuG-Flow: A Large-Scale Synthetic Dataset of Diverse Intracranial Aneurysm Geometries and Hemodynamics
- NeurIPS 2025Approximation and Generalization Abilities of Score-based Neural Network Generative Models for Sub-Gaussian Distributions
- NeurIPS 2025Assessing the quality of denoising diffusion models in Wasserstein distance: noisy score and optimal bounds
- NeurIPS 2025Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
- NeurIPS 2025AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and Fluid–Structure Interaction
- NeurIPS 2025Balanced Conic Rectified Flow
- NeurIPS 2025Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
- NeurIPS 2025Beyond Scores: Proximal Diffusion Models
- NeurIPS 2025BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints
- NeurIPS 2025Bilevel Optimization for Adversarial Learning Problems: Sharpness, Generation, and Beyond
- NeurIPS 2025BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction
- NeurIPS 2025BitMark: Watermarking Bitwise Autoregressive Image Generative Models
- NeurIPS 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
- NeurIPS 2025Boosting Generative Image Modeling via Joint Image-Feature Synthesis
- NeurIPS 2025BrainFlow: A Holistic Pathway of Dynamic Neural System on Manifold
- NeurIPS 2025CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
- NeurIPS 2025CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
- NeurIPS 2025Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies
- NeurIPS 2025CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis
- NeurIPS 2025ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
- NeurIPS 2025Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
- NeurIPS 2025Composition and Alignment of Diffusion Models using Constrained Learning
- NeurIPS 2025Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
- NeurIPS 2025Compressed and Smooth Latent Space for Text Diffusion Modeling
- NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
- NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
- NeurIPS 2025Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning
- NeurIPS 2025Continuity and Isolation Lead to Doubts or Dilemmas in Large Language Models
- NeurIPS 2025Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
- NeurIPS 2025Controllable Human-centric Keyframe Interpolation with Generative Prior
- NeurIPS 2025CORAL: Disentangling Latent Representations in Long-Tailed Diffusion
- NeurIPS 2025Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
- NeurIPS 2025Counterfactual Identifiability via Dynamic Optimal Transport
- NeurIPS 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridge
- NeurIPS 2025Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training
- NeurIPS 2025DeblurDiff: Real-Word Image Deblurring with Generative Diffusion Models
- NeurIPS 2025DeCaFlow: A deconfounding causal generative model
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接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查深度学习 · generative models and autoencoders在 NeurIPS 2023–2026 的论文趋势,哪一年涨得最快?
AI 会调用的工具:get_topic_stats {"topic": "dl-generative-models-and-autoencoders"}
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。