Z
Zhihui Li
Researcher at University of New South Wales
Publications - 55
Citations - 2652
Zhihui Li is an academic researcher from University of New South Wales. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 18, co-authored 49 publications receiving 1195 citations. Previous affiliations of Zhihui Li include Shandong University & University of Technology, Sydney.
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A Survey of Deep Active Learning
TL;DR: A formal classification method for the existing work in deep active learning is provided, along with a comprehensive and systematic overview, to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL.
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A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
TL;DR: This survey provides a new perspective on the NAS starting with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these earlyNAS algorithms, and then giving solutions for subsequent related research work.
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Visual saliency guided complex image retrieval
TL;DR: A novel visual saliency guided complex image retrieval model is proposed which addresses the complexity of the images and proposes a two-stage definition: Cognitive load based complexity and Cognitive level of complexity classification.
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Rank-Constrained Spectral Clustering With Flexible Embedding
TL;DR: This work proposes a rank-constrained SC with flexible embedding framework, which is superior to previous SC methods in that the block-diagonal affinity matrix learned simultaneously with the adaptive graph construction process, more explicitly induces the cluster membership without further discretization.
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A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
TL;DR: A comprehensive and systematic survey on neural architecture search is provided in this article, which provides an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms and then providing solutions for subsequent related research work.