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Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
Yong Luo, Dacheng Tao, Chang Xu +3
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several obj…
Towards Evolutional Compression
Yunhe Wang, Chang Xu, Jiayan Qiu +2
Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly reco…
Privileged Multi-label Learning
Shan You, Chang Xu, Yunhe Wang +2
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individ…
Streaming View Learning
Chang Xu, Dacheng Tao, Chao Xu
An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed. However, due to various factors when collecting and pre-pr…
Streaming Label Learning for Modeling Labels on the Fly
Shan You, Chang Xu, Yunhe Wang +2
It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning proce…