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 20 / 27 页

951. ICML 2024Light and Optimal Schrödinger Bridge Matching
952. ICML 2024Listening to the noise: Blind Denoising with Gibbs Diffusion
953. ICML 2024Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
954. ICML 2024Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
955. ICML 2024Make-A-Shape: a Ten-Million-scale 3D Shape Model
956. ICML 2024Masked Face Recognition with Generative-to-Discriminative Representations
957. ICML 2024Mean-field Chaos Diffusion Models
958. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
959. ICML 2024Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems
960. ICML 2024Nearest Neighbour Score Estimators for Diffusion Generative Models
961. ICML 2024Neural Diffusion Models
962. ICML 2024NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction
963. ICML 2024ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
964. ICML 2024On Mechanistic Knowledge Localization in Text-to-Image Generative Models
965. ICML 2024On the Role of Edge Dependency in Graph Generative Models
966. ICML 2024OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
967. ICML 2024Partial Optimality in the Linear Ordering Problem
968. ICML 2024Particle Denoising Diffusion Sampler
969. ICML 2024PcLast: Discovering Plannable Continuous Latent States
970. ICML 2024Position: A Safe Harbor for AI Evaluation and Red Teaming
971. ICML 2024Position: Compositional Generative Modeling: A Single Model is Not All You Need
972. ICML 2024Position: Data-driven Discovery with Large Generative Models
973. ICML 2024Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
974. ICML 2024Position: Explain to Question not to Justify
975. ICML 2024Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
976. ICML 2024Positive and Unlabeled Learning with Controlled Probability Boundary Fence
977. ICML 2024Preventing Model Collapse in Gaussian Process Latent Variable Models
978. ICML 2024Pricing with Contextual Elasticity and Heteroscedastic Valuation
979. ICML 2024Projecting Molecules into Synthesizable Chemical Spaces
980. ICML 2024Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
981. ICML 2024Provably Robust DPO: Aligning Language Models with Noisy Feedback
982. ICML 2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
983. ICML 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
984. ICML 2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
985. ICML 2024Recovering the Pre-Fine-Tuning Weights of Generative Models
986. ICML 2024Reflected Flow Matching
987. ICML 2024Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
988. ICML 2024Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
989. ICML 2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
990. ICML 2024RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
991. ICML 2024Robust and Conjugate Gaussian Process Regression
992. ICML 2024Sample as you Infer: Predictive Coding with Langevin Dynamics
993. ICML 2024Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
994. ICML 2024Self-Correcting Self-Consuming Loops for Generative Model Training
995. ICML 2024Single-Model Attribution of Generative Models Through Final-Layer Inversion
996. ICML 2024Sliced Wasserstein with Random-Path Projecting Directions
997. ICML 2024SMaRt: Improving GANs with Score Matching Regularity
998. ICML 2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models
999. ICML 2024SqueezeLLM: Dense-and-Sparse Quantization
1000. ICML 2024Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms

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。