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 768 篇，第 15 / 16 页

701. NeurIPS 2024Simple and Fast Distillation of Diffusion Models
702. NeurIPS 2024Simplified and Generalized Masked Diffusion for Discrete Data
703. NeurIPS 2024Skinned Motion Retargeting with Dense Geometric Interaction Perception
704. NeurIPS 2024SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction
705. NeurIPS 2024Soft ascent-descent as a stable and flexible alternative to flooding
706. NeurIPS 2024Sparse Bayesian Generative Modeling for Compressive Sensing
707. NeurIPS 2024Sparse High Rank Adapters
708. NeurIPS 2024Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis
709. NeurIPS 2024SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models
710. NeurIPS 2024Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
711. NeurIPS 2024Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness
712. NeurIPS 2024Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
713. NeurIPS 2024Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
714. NeurIPS 2024Statistical Efficiency of Distributional Temporal Difference Learning
715. NeurIPS 2024Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
716. NeurIPS 2024Studying How to Efficiently and Effectively Guide Models with Explanations - A Reproducibility Study
717. NeurIPS 2024Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass
718. NeurIPS 2024SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
719. NeurIPS 2024Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
720. NeurIPS 2024Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
721. NeurIPS 2024Taming Generative Diffusion Prior for Universal Blind Image Restoration
722. NeurIPS 2024Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
723. NeurIPS 2024Tetrahedron Splatting for 3D Generation
724. NeurIPS 2024The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
725. NeurIPS 2024The GAN is dead; long live the GAN! A Modern GAN Baseline
726. NeurIPS 2024The tree autoencoder model, with application to hierarchical data visualization
727. NeurIPS 2024Theoretical guarantees in KL for Diffusion Flow Matching
728. NeurIPS 2024Thought of Search: Planning with Language Models Through The Lens of Efficiency
729. NeurIPS 2024Time-Reversal Provides Unsupervised Feedback to LLMs
730. NeurIPS 2024Towards a Scalable Reference-Free Evaluation of Generative Models
731. NeurIPS 2024Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
732. NeurIPS 2024Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
733. NeurIPS 2024Training Data Attribution via Approximate Unrolling
734. NeurIPS 2024TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks
735. NeurIPS 2024Transcendence: Generative Models Can Outperform The Experts That Train Them
736. NeurIPS 2024Transcoders find interpretable LLM feature circuits
737. NeurIPS 2024Transferring disentangled representations: bridging the gap between synthetic and real images
738. NeurIPS 2024Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
739. NeurIPS 2024Transformer Doctor: Diagnosing and Treating Vision Transformers
740. NeurIPS 2024Transformers Can Do Arithmetic with the Right Embeddings
741. NeurIPS 2024Transformers on Markov data: Constant depth suffices
742. NeurIPS 2024Truncated Variance Reduced Value Iteration
743. NeurIPS 2024TSDS: Data Selection for Task-Specific Model Finetuning
744. NeurIPS 2024UDPM: Upsampling Diffusion Probabilistic Models
745. NeurIPS 2024Understanding Hallucinations in Diffusion Models through Mode Interpolation
746. NeurIPS 2024Understanding Information Storage and Transfer in Multi-Modal Large Language Models
747. NeurIPS 2024UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
748. NeurIPS 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
749. NeurIPS 2024Unity by Diversity: Improved Representation Learning for Multimodal VAEs
750. NeurIPS 2024Universal In-Context Approximation By Prompting Fully Recurrent Models

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。