Imperfect score-matching leads to a shift between the training and the sampling distribution of diffusion models. Due to the recursive nature of the generation process, errors in previous steps yield sampling iterates that drift away from the training distribution. Yet, the standard training objective via Denoising Score Matching (DSM) is only designed to optimize over non-drifted data. To train on drifted data, we propose to enforce a \emph{consistency} property which states that predictions of the model on its own generated data are consistent across time. Theoretically, we show that if the score is learned perfectly on some non-drifted points (via DSM) and if the consistency property is enforced everywhere, then the score is learned accurately everywhere. Empirically we show that our novel training objective yields state-of-the-art results for conditional and unconditional generation in CIFAR-10 and baseline improvements in AFHQ and FFHQ. We open-source our code and models: https://github.com/giannisdaras/cdm
Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling
Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea（计算机科学与工程系，首尔国立大学，韩国首尔）；School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland（计算机与通信科学学院，瑞士洛桑联邦理工学院）；Meta Reality Labs, Redmond, USA（Meta现实实验室，美国西雅图）
Mitigating Exposure Bias in Discriminator Guided Diffusion Models
Artificial Intelligence and Learning Systems Laboratory（人工智能与学习系统实验室）；School of Electrical and Computer Engineering（电气与计算机工程学院）；National Technical University of Athens（雅典国立技术大学）
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
University of Bonn（波恩大学）；Helmholtz Institute for Radiation and Nuclear Physics (HISKP)（亥姆霍兹辐射与核物理研究所）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；Northeastern University - London（东北大学伦敦校区）；Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器研究所）；Roche（罗氏公司）；RIKEN Center for AIP（理化学研究所先进智能研究中心）
Improving the Euclidean Diffusion Generation of Manifold Data by Mitigating Score Function Singularity
Center for Data Science, Peking University（北京大学数据科学中心）；Zuse Institute Berlin（柏林泽尼茨研究所）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；LMAM and School of Mathematical Sciences, Peking University（北京大学数学科学学院）
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics & Astronautics（南京航空航天大学工信部模式分析与机器智能重点实验室）
PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Southeast University, Nanjing, China（南京东南大学）；Wuhan AI Research, Wuhan, China（武汉人工智能研究院）
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Meituan（美团）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所）；Key Laboratory of Intelligent Information Processing, Fudan University（复旦大学智能信息处理重点实验室）；Macau University of Science and Technology（澳门科技大学）
Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling
VinUniversity（文大学）；VinUni-Illinois Smart Health Center（VinUni-伊利诺伊智能健康中心）；University of Stuttgart（斯图加特大学）；University of California Los Angeles (UCLA)（加州大学洛杉矶分校）；International University - VNUHCM（国际大学 - VNUHCM）；University of Science - VNUHCM（科学大学 - VNUHCM）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Oldenburg University（奥尔登堡大学）；Max Planck Research School for Intelligent Systems (IMPRS-IS)（马克斯·普朗克智能系统研究学校）
CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction
College of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）
Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation
Nanjing University（南京大学）；Australia National University（澳大利亚国立大学）；Wuhan University（武汉大学）；National University of Singapore（新加坡国立大学）；University of Technology Sydney（技术科技大学）
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
CortAIx Labs, Thales Deutschland（Thales Deutschland）；Grey Solutions；University of Osnabrück（奥斯纳布吕克大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Ulm University（乌尔姆大学）
