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 4,314 篇，第 20 / 87 页

951. ICML 2026Semantic Granularity Navigation in Image Editing
952. ICML 2026Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression
953. ICML 2026Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation
954. ICML 2026SemanticNVS: Improving Semantic Scene Understanding in Generative Novel View Synthesis
955. ICML 2026Set Diffusion: Interpolating Token Orderings between Autoregression and Diffusion for Fast and Flexible Decoding
956. ICML 2026SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
957. ICML 2026Sharper Generalization Guarantees for Asynchronous SGD: Beyond Lipschitzness, Smoothness and Data Homogeneity
958. ICML 2026Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
959. ICML 2026Simple Denoising Diffusion Language Models
960. ICML 2026SJD-SV: Speculative Jacobi Decoding with Semantics Verification for Autoregressive Image Generation
961. ICML 2026Skipping the Zeros in Diffusion Models for Sparse Data Generation
962. ICML 2026SlerpFlow: Spherical Trajectory Correction for Rectified Flow Inversion
963. ICML 2026SMART: Scalable Mesh‑free Aerodynamic Simulations from Raw Geometries using a Transformer‑based Surrogate Model
964. ICML 2026Solving Inverse Problems with Flow-based Models via Model Predictive Control
965. ICML 2026Sparse Autoencoders are Topic Models
966. ICML 2026Sparse Autoencoders for Interpretable Emotion Control in Text-to-Speech
967. ICML 2026Sparse Bayesian Deep Functional Learning with Structured Region Selection
968. ICML 2026Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
969. ICML 2026Spatiotemporal Imputation with Graph-Informed Flow Matching
970. ICML 2026SpecPL: Disentangling Spectral Granularity for Prompt Learning
971. ICML 2026Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
972. ICML 2026Spectral Guidance for Flexible and Efficient Control of Diffusion Models
973. ICML 2026Spectrally-Guided Diffusion Noise Schedules
974. ICML 2026Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation
975. ICML 2026SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models
976. ICML 2026Stabilizing Reinforcement Learning for Diffusion Language Models
977. ICML 2026Stable Velocity: A Variance Perspective on Flow Matching
978. ICML 2026Statistical Consistency and Generalization of Contrastive Representation Learning
979. ICML 2026Steer Like the LLM: Activation Steering that Mimics Prompting
980. ICML 2026Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
981. ICML 2026Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
982. ICML 2026Strategic Candidacy in Generative AI Arenas
983. ICML 2026StructMAR: Structure-Aware Masked Autoregression for Explicit Layout Alignment in Text-to-Image Generation
984. ICML 2026Structurally Aligned Subtask-Level Memory for Software Engineering Agents
985. ICML 2026Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges
986. ICML 2026Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
987. ICML 2026SVRG and Beyond via Posterior Correction
988. ICML 2026Symbol-Equivariant Recurrent Reasoning Models
989. ICML 2026SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
990. ICML 2026SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
991. ICML 2026TAG: Tangential Amplifying Guidance for Hallucination-Resistant Sampling
992. ICML 2026Tail Annealing for Heavy-Tailed Flow Matching
993. ICML 2026Taming Aleatoric Impulse in Off-Policy Reinforcement Learning
994. ICML 2026Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance
995. ICML 2026Temperature Scaling in Discrete Sequence (Language) Models
996. ICML 2026Temporal Difference Learning for Diffusion Models
997. ICML 2026Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
998. ICML 2026Test-Time Anchoring for Discrete Diffusion Posterior Sampling
999. ICML 2026Test-Time Guidance for Flow-Based Generative Models via Parallel Tempering on Source Distributions
1000. ICML 2026Test-Time Reinforcement Learning for Flow Matching

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。