J
Jian Zhang
Researcher at University of Technology, Sydney
Publications - 469
Citations - 9030
Jian Zhang is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Feature extraction & Computer science. The author has an hindex of 41, co-authored 419 publications receiving 6906 citations. Previous affiliations of Jian Zhang include Beijing Institute of Technology & Commonwealth Scientific and Industrial Research Organisation.
Papers
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Journal ArticleDOI
Adversarial Domain Adaptation with Domain Mixup
TL;DR: This paper presents adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
Journal ArticleDOI
Massive hybrid antenna array for millimeter-wave cellular communications
TL;DR: This article investigates how the hybrid array architecture and special mm-Wave channel property can be exploited to design suboptimal but practical massive antenna array schemes and compares two main types of hybrid arrays, interleaved and localized arrays, and recommends that the localized array is a better option in terms of overall performance and hardware feasibility.
Journal ArticleDOI
Global and Local Structure Preservation for Feature Selection
TL;DR: This paper proposes a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection and shows that the best feature selection performance is always obtained when the two factors are appropriately integrated.
Journal ArticleDOI
A cell-loss concealment technique for MPEG-2 coded video
TL;DR: It is demonstrated that the new approach can significantly increase received video quality, but at the cost of a considerable computational overhead, and the technique is extended to allow for higher computational efficiency.
Proceedings ArticleDOI
Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image
TL;DR: The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.