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 900 篇，第 13 / 18 页

601. ICML 2025SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
602. ICML 2025SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
603. ICML 2025SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion
604. ICML 2025Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
605. ICML 2025Scaling Laws for Pre-training Agents and World Models
606. ICML 2025Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
607. ICML 2025Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
608. ICML 2025Selective Response Strategies for GenAI
609. ICML 2025Self-Consuming Generative Models with Adversarially Curated Data
610. ICML 2025Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
611. ICML 2025Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
612. ICML 2025Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
613. ICML 2025SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation
614. ICML 2025Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation
615. ICML 2025Smooth Interpolation for Improved Discrete Graph Generative Models
616. ICML 2025SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
617. ICML 2025Sparse Autoencoders, Again?
618. ICML 2025Spatial Reasoning with Denoising Models
619. ICML 2025Spherical-Nested Diffusion Model for Panoramic Image Outpainting
620. ICML 2025SSHR: More Secure Generative Steganography with High-Quality Revealed Secret Images
621. ICML 2025Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness
622. ICML 2025Stochastic Forward–Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets
623. ICML 2025Symmetry-Aware GFlowNets
624. ICML 2025TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data
625. ICML 2025TabSDS: a Lightweight, Fully Non-Parametric, and Model Free Approach for Generating Synthetic Tabular Data
626. ICML 2025Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion
627. ICML 2025Teaching Language Models to Critique via Reinforcement Learning
628. ICML 2025Test-Time Graph Neural Dataset Search With Generative Projection
629. ICML 2025Testing Conditional Mean Independence Using Generative Neural Networks
630. ICML 2025The Diffusion Duality
631. ICML 2025TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
632. ICML 2025ToMA: Token Merge with Attention for Diffusion Models
633. ICML 2025Towards a Mechanistic Explanation of Diffusion Model Generalization
634. ICML 2025Towards a Unified Framework of Clustering-based Anomaly Detection
635. ICML 2025Tractable Transformers for Flexible Conditional Generation
636. ICML 2025Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
637. ICML 2025Training Diffusion-based Generative Models with Limited Data
638. ICML 2025Tree-Sliced Wasserstein Distance with Nonlinear Projection
639. ICML 2025Tree-Sliced Wasserstein Distance: A Geometric Perspective
640. ICML 2025Ultra-Resolution Adaptation with Ease
641. ICML 2025Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
642. ICML 2025Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
643. ICML 2025UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
644. ICML 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
645. ICML 2025Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
646. ICML 2025Variational Control for Guidance in Diffusion Models
647. ICML 2025Variational Learning of Fractional Posteriors
648. ICML 2025Variational Rectified Flow Matching
649. ICML 2025VCT: Training Consistency Models with Variational Noise Coupling
650. ICML 2025Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision

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。