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

• URL: http://arxiv.org/abs/2303.08358v2
• Date: Thu, 23 Mar 2023 03:09:11 GMT
• Title: DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
• Authors: Chengliang Liu, Jie Wen, Xiaoling Luo, Chao Huang, Zhihao Wu, Yong Xu
• Abstract summary: Multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation. We propose a deep instance-level contrastive network, namely DICNet, to deal with the double incomplete multi-view multi-label classification problem. 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.
• Score: 20.892833511657166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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