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 830 篇，第 17 / 17 页

801. ICLR 2023Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
802. ICLR 2023Neural Implicit Shape Editing using Boundary Sensitivity
803. ICLR 2023Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
804. ICLR 2023Novel View Synthesis with Diffusion Models
805. ICLR 2023On Accelerated Perceptrons and Beyond
806. ICLR 2023OPTQ: Accurate Quantization for Generative Pre-trained Transformers
807. ICLR 2023Perfectly Secure Steganography Using Minimum Entropy Coupling
808. ICLR 2023Pitfalls of Gaussians as a noise distribution in NCE
809. ICLR 2023Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
810. ICLR 2023Quantized Compressed Sensing with Score-Based Generative Models
811. ICLR 2023Revisiting adapters with adversarial training
812. ICLR 2023Robust and Controllable Object-Centric Learning through Energy-based Models
813. ICLR 2023Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
814. ICLR 2023Semi-supervised learning with a principled likelihood from a generative model of data curation
815. ICLR 2023Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
816. ICLR 2023Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
817. ICLR 2023STaSy: Score-based Tabular data Synthesis
818. ICLR 2023Structure by Architecture: Structured Representations without Regularization
819. ICLR 2023Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
820. ICLR 2023TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
821. ICLR 2023The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
822. ICLR 2023The Role of ImageNet Classes in Fréchet Inception Distance
823. ICLR 2023Trading Information between Latents in Hierarchical Variational Autoencoders
824. ICLR 2023Transformers are Sample-Efficient World Models
825. ICLR 2023Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
826. ICLR 2023Verifying the Union of Manifolds Hypothesis for Image Data
827. ICLR 2023Weighted Clock Logic Point Process
828. ICLR 2023Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions
829. ICLR 2023Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic
830. ICLR 2023Write and Paint: Generative Vision-Language Models are Unified Modal Learners

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。