While deep neural networks demonstrate state-of-the-art performance on a variety of learning tasks, their performance relies on the assumption that train and test distributions are the same, which may not hold in real-world applications. Domain generalization addresses this issue by employing multiple source domains to build robust models that can generalize to unseen target domains subject to shifts in data distribution. In this paper, we propose Domain-Free Domain Generalization (DFDG), a model-agnostic method to achieve better generalization performance on the unseen test domain without the need for source domain labels. DFDG uses novel strategies to learn domain-invariant class-discriminative features. It aligns class relationships of samples through class-conditional soft labels, and uses saliency maps, traditionally developed for post-hoc analysis of image classification networks, to remove superficial observations from training inputs. DFDG obtains competitive performance on both time series sensor and image classification public datasets.
SHED: Style-Homogenized Embedding Alignment for Domain Generalization
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China（1 东南大学计算机科学与工程学院，南京 210096，中国）；Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China（2 教育部计算机网络与信息集成重点实验室（东南大学），中国）
Pretrain-Test Task Alignment Governs Generalization in In-Context Learning
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA（哈佛大学约翰·A·保罗森工程与应用科学学院）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA（哈佛大学自然与人工智能研究 institute）；Society of Fellows, Harvard University, Cambridge, MA, USA（哈佛大学 fellows 社会）；Center for Brain Science, Harvard University, Cambridge, MA, USA（哈佛大学脑科学中心）
Alignment-Aware Model Adaptation via Feedback-Guided Optimization
Department of Computer Science, University of British Columbia, Location, Canada（不列颠哥伦比亚大学计算机科学系）；Vector Institute of AI, Toronto, Canada（人工智能向量研究所）；Stony Brook University, New York, USA（石溪大学）
Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding
Université Paris-Saclay（巴黎-萨克雷大学）；Inria（法国国家信息与自动化技术研究院）；CEA（法国原子能委员会）；Cognitive Neuroimaging Unit（认知神经成像单位）；Institut National de la Santé et de la Recherche Médicale（法国国家卫生与医学研究院）；Commissariat à l’Energie Atomique et aux énergies alternatives（法国原子能委员会及替代能源委员会）；NeuroSpin center（神经科学中心）；Institut de neuromodulation（神经调制研究所）；Université Paris Cité（巴黎-城市大学）
Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition
School of Biological Science and Medical Engineering, Hunan University of Technology（湖南工业大学生物科学与医学工程学院）；School of Biomedical Engineering, Health Science Center, Shenzhen University（深圳大学医学部生物医学工程学院）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；Shenzhen Pengrui Brain Science Technology（深圳鹏瑞脑科学技术有限公司）
Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization
South China University of Technology（华南理工大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Ocean University of China（中国海洋大学）；Center for AI Theoretical Foundation and Systems, Shenzhen Loop Area Institute（深圳环区研究所人工智能理论基础与系统中心）
Predicting Alignment Generalization with Value Representations
Carnegie Mellon University（卡内基梅隆大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（苏黎世联邦理工学院人工智能中心）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（矢量研究所）
Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking
School of Software, Shandong University（山东大学软件学院）；Joint SDU–NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-南洋理工大学人工智能联合研究中心（C-FAIR））；Nanyang Technological University（南洋理工大学）
PromptHub: Enhancing Multi-Prompt Visual In-Context Learning with Locality-Aware Fusion, Concentration and Alignment
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳分校）；Meituan, Beijing（美团，北京）
Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment
Nanjing University（南京大学）；Airon Technology CO., LTD（艾润科技有限公司）；Shanghai Jiao Tong University（上海交通大学）；University of Bristol（布里斯托大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）；Carnegie Mellon University（卡内基梅隆大学）
MVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion
University of Shanghai for Science and TechnologyChina（上海科学技术大学中国）；Shenzhen Institute of Advanced Technology, Chinese Academy of SciencesChina（深圳先进技术研究院，中国科学院中国）；University of Minnesota-Twin Cities, USA（明尼苏达大学双城分校，美国）；University of LeedsUK（利兹大学，英国）
