DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means that not only multi-view features are often missing, and label completeness is also difficult to be satisfied. To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet. Different from conventional methods, our DICNet focuses on leveraging deep neural network to exploit the high-level semantic representations of samples rather than shallow-level features. First, we utilize the stacked autoencoders to build an end-to-end multi-view feature extraction framework to learn the view-specific representations of samples. Furthermore, in order to improve the consensus representation ability, we introduce an incomplete instance-level contrastive learning scheme to guide the encoders to better extract the consensus information of multiple views and use a multi-view weighted fusion module to enhance the discrimination of semantic features. Overall, our DICNet is adept in capturing consistent discriminative representations of multi-view multi-label data and avoiding the negative effects of missing views and missing labels. Extensive experiments performed on five datasets validate that our method outperforms other state-of-the-art methods.

Authors

• Chengliang Liu Computer Science and Engineering, Hong Kong University of Science and Technology
• Jie Wen
• Xiaoling Luo Shenzhen University
• Chao Huang Tongji University
• Zhihao Wu School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen
• Yong Xu

Keywords

• ML: Multi-class/Multi-label Learning & Extreme Classification
• ML: Multi-instance/Multi-view Learning
• ML: Multimodal Learning
• ML: Representation Learning