The Hong Kong University of Science and Technology (Guangzhou) Data Science and Analytics Thrust, Information Hub PhD Qualifying Examination Title: "Self-Supervised Representation Learning" By Mr. Xiasi WANG Abstract The effectiveness of deep supervised learning heavily depends on the availability of large amounts of labeled data, which
can be expensive to obtain. As a subset of unsupervised learning, self-supervised learning (SSL) has recently garnered
significant attention due to its remarkable ability to learn useful features from large collections of unlabeled data. This
survey provides a comprehensive review of various SSL methods with a specific focus on computer vision (CV). The reviewed
SSL methods are categorized into three types based on their respective pretext tasks. In addition, the multi-view learning
framework within the SSL is reviewed and discussed. Based on this framework, a novel method is proposed for learning minimal
sufficient representation. Finally, future research directions of SSL are discussed. Date: Thursday, 26 Oct 2023 Time: 16:00pm - 17:00pm Venue: Zoom ID 863 0445 5450 Password: 2023 Chairperson: Dr Xin WANG(AI) Committee Members: Prof. Yuan YAO (Prime Supervisor) Prof. Lianwen ZHANG (Co-Supervisor) Dr. Wenjia WANG (DSA)