Learning to Learn with Variational Information Bottleneck for Domain Generalization
University of Amsterdam（阿姆斯特丹大学）；Nankai University（南开大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Duke University（杜克大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Yingjun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees G. M. Snoek, Ling Shao
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn domain-invariant representations by the proposed principle of meta variational information bottleneck, we call MetaVIB. MetaVIB is derived from novel variational bounds of mutual information, by leveraging the meta-learning setting of domain generalization. Through episodic training, MetaVIB learns to gradually narrow domain gaps to establish domain-invariant representations, while simultaneously maximizing prediction accuracy. We conduct experiments on three benchmarks for cross-domain visual recognition. Comprehensive ablation studies validate the benefits of MetaVIB for domain generalization. The comparison results demonstrate our method outperforms previous approaches consistently.
VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China（复旦大学计算机科学与人工智能学院，上海，中国）；Shanghai Key Laboratory of Intelligent Information Processing（上海智能信息处理重点实验室）
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Harvard Medical School（哈佛医学院）；Wuhan University（武汉大学）；Bowling Green State University（鲍灵格林州立大学）；University of California, Irvine（加州大学欧文分校）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
East China Normal University（华东师范大学）；Huawei Technologies（华为技术有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Xiamen University（厦门大学）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen University（深圳大学）；University of Sydney（悉尼大学）
VNDUQE: Information-Theoretic Novelty Detection using Deep Variational Information Bottleneck
1 Nondestructive Evaluation Lab, Department of Electrical；Computer Engineering Michigan State University East Lansing, MI Email；2 Department of Electrical
Variational Adapter for Cross-modal Similarity Representation
School of Remote Sensing and Information Engineering（遥感与信息工程学院）；Wuhan University（武汉大学）；School of Data Science and Engineering（数据科学与工程学院）；East China Normal University（华东师范大学）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；National University of Singapore（新加坡国立大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
