1. Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
- Author
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Lu, Xiaohuan, Zhao, Lian, Wong, Wai Keung, Wen, Jie, Long, Jiang, and Xie, Wulin
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs the comprehensive understanding of samples, omitting crucial details essential for classification. To address this issue, we present a task-augmented cross-view imputation network (TACVI-Net) for the purpose of handling partial multi-view incomplete multi-label classification. Specifically, we employ a two-stage network to derive highly task-relevant features to recover the missing views. In the first stage, we leverage the information bottleneck theory to obtain a discriminative representation of each view by extracting task-relevant information through a view-specific encoder-classifier architecture. In the second stage, an autoencoder based multi-view reconstruction network is utilized to extract high-level semantic representation of the augmented features and recover the missing data, thereby aiding the final classification task. Extensive experiments on five datasets demonstrate that our TACVI-Net outperforms other state-of-the-art methods.
- Published
- 2024