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Ivor W. Tsang
Researcher at University of Technology, Sydney
Publications - 361
Citations - 22076
Ivor W. Tsang is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 64, co-authored 322 publications receiving 18649 citations. Previous affiliations of Ivor W. Tsang include Hong Kong University of Science and Technology & Agency for Science, Technology and Research.
Papers
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Proceedings ArticleDOI
Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning
TL;DR: This paper presents a novel long-short distance aggregation networks (\textttLSDAN) for positive unlabeled (PU) graph learning, to generate multiple graphs at different distances based on the adjacency matrix, and further develop a long- short distance attention model for these graphs.
Journal ArticleDOI
Complementary Attributes: A New Clue to Zero-Shot Learning
TL;DR: Zhang et al. as mentioned in this paper introduced the complementary attributes (CAs) as a supplement to the original attributes to enhance the semantic representation ability of ZSL models, which can improve the PAC-style generalization bound of the original ZSL model.
Journal ArticleDOI
Understanding Deep Representations Learned in Modeling Users Likes
TL;DR: A deep bi-modal knowledge representation of images based on their visual content and associated tags (text) is presented, and a mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities.
Book ChapterDOI
Diversified SVM ensembles for large data sets
TL;DR: In this article, the orthogonality constraints were incorporated in the core vector machine (CVM) ensembles to speed up the maximum margin discriminant analysis (MMDA) algorithm.
Posted Content
Copy and Paste GAN: Face Hallucination from Shaded Thumbnails
TL;DR: A Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination and alleviating the correspondence ambiguity between LR inputs and external HR inputs is proposed.