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 18 / 27 页

851. ICML 2025ToMA: Token Merge with Attention for Diffusion Models
852. ICML 2025Towards a Mechanistic Explanation of Diffusion Model Generalization
853. ICML 2025Towards a Unified Framework of Clustering-based Anomaly Detection
854. ICML 2025Tractable Transformers for Flexible Conditional Generation
855. ICML 2025Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
856. ICML 2025Training Diffusion-based Generative Models with Limited Data
857. ICML 2025Tree-Sliced Wasserstein Distance with Nonlinear Projection
858. ICML 2025Tree-Sliced Wasserstein Distance: A Geometric Perspective
859. ICML 2025Ultra-Resolution Adaptation with Ease
860. ICML 2025Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
861. ICML 2025Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
862. ICML 2025UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
863. ICML 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
864. ICML 2025Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
865. ICML 2025Variational Control for Guidance in Diffusion Models
866. ICML 2025Variational Learning of Fractional Posteriors
867. ICML 2025Variational Rectified Flow Matching
868. ICML 2025VCT: Training Consistency Models with Variational Noise Coupling
869. ICML 2025Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
870. ICML 2025VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
871. ICML 2025VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
872. ICML 2025Visual Generation Without Guidance
873. ICML 2025Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
874. ICML 2025WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models
875. ICML 2025What Limits Bidirectional Model's Generative Capabilities? A Uni-Bi-Directional Mixture-of-Expert Method For Bidirectional Fine-tuning
876. ICML 2025WMAdapter: Adding WaterMark Control to Latent Diffusion Models
877. ICML 2025Wyckoff Transformer: Generation of Symmetric Crystals
878. ICML 2025Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
879. ICML 2025ZipAR: Parallel Autoregressive Image Generation through Spatial Locality
880. ICML 2024A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
881. ICML 2024A Distributional Analogue to the Successor Representation
882. ICML 2024A Fixed-Point Approach for Causal Generative Modeling
883. ICML 2024A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
884. ICML 2024Accelerated Speculative Sampling Based on Tree Monte Carlo
885. ICML 2024Accelerating Convergence of Score-Based Diffusion Models, Provably
886. ICML 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
887. ICML 2024Adaptive Accompaniment with ReaLchords
888. ICML 2024Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework
889. ICML 2024Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
890. ICML 2024AI Alignment with Changing and Influenceable Reward Functions
891. ICML 2024An Interpretable Evaluation of Entropy-based Novelty of Generative Models
892. ICML 2024Attribute Based Interpretable Evaluation Metrics for Generative Models
893. ICML 2024Balanced Resonate-and-Fire Neurons
894. ICML 2024Batch and match: black-box variational inference with a score-based divergence
895. ICML 2024Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation
896. ICML 2024Bottleneck-Minimal Indexing for Generative Document Retrieval
897. ICML 2024Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
898. ICML 2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
899. ICML 2024Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
900. ICML 2024Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth

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。