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.

本论文提出了一种名为 DICNet 的深度实例级对比网络，能够有效地应对数据收集和手动注释的不确定因素所导致的多视图多标签数据缺失的问题。通过利用深度神经网络，DICNet 能够更好地提取多视图样本的高级语义表征，并在此基础上采取一系列手段来提高平稳性、加强语义特征分辨率。大量实验结果表明，本方法能够比其他最先进的方法更好地捕捉一致且有区别的多视图多标签数据的表示特征。