C
Chang Xu
Researcher at University of Sydney
Publications - 467
Citations - 13012
Chang Xu is an academic researcher from University of Sydney. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 42, co-authored 260 publications receiving 7189 citations. Previous affiliations of Chang Xu include University of Melbourne & Information Technology University.
Papers
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A Survey on Multi-view Learning
Chang Xu,Dacheng Tao,Chao Xu +2 more
TL;DR: By exploring the consistency and complementary properties of different views, multi-View learning is rendered more effective, more promising, and has better generalization ability than single-view learning.
Proceedings ArticleDOI
GhostNet: More Features From Cheap Operations
Abstract: Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet.
Posted Content
GhostNet: More Features from Cheap Operations
TL;DR: A novel Ghost module is proposed to generate more feature maps from cheap operations based on a set of intrinsic feature maps to generate many ghost feature maps that could fully reveal information underlying intrinsic features.
Posted Content
Pre-Trained Image Processing Transformer
Hanting Chen,Yunhe Wang,Tianyu Guo,Chang Xu,Yiping Deng,Zhenhua Liu,Siwei Ma,Chunjing Xu,Chao Xu,Wen Gao +9 more
TL;DR: To maximally excavate the capability of transformer, the IPT model is presented to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs and the contrastive learning is introduced for well adapting to different image processing tasks.
Proceedings ArticleDOI
STRIP: a defence against trojan attacks on deep neural networks
TL;DR: This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system, which achieves an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers.