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.

该研究解决了多视图多标签学习中由于数据收集和标注不可靠导致的不完整训练数据问题。提出的任务增强跨视图插补网络（TACVI-Net）通过两阶段网络提取高度任务相关特征，以恢复缺失视图。在五个数据集上的广泛实验结果表明，该方法优于其他最先进的方法，具有重要的应用潜力。

任务增强的跨视图插补网络用于部分多视图不完整多标签分类