Attention regularized semi-supervised learning with class-ambiguous data for image classification

Data augmentation via randomly combining training instances and interpolating the corresponding labels has shown impressive gains in image classification. However, model attention regions are not necessarily meaningful in class semantics, especially for the case of limited supervision. In this paper, we present a semi-supervised classification model based on Class-Ambiguous Data with Attention Regularization, which is referred to as CADAR. Specifically, we adopt a Random Regional Interpolation (RRI) module to construct complex and effective class-ambiguous data, such that the model behavior can be regularized around decision boundaries. By aggregating the parameters of a classification network over training epochs to produce more reliable predictions on unlabeled data, RRI can also be applied to them as well as labeled data. Further, the classifier is enforced to apply consistent attention on the original and constructed data. This is important for inducing the model to learn discriminative features from the class-related regions. The experiment results demonstrate that CADAR significantly benefits from the constructed data and attention regularization, and thus achieves superior performance across multiple standard benchmarks and different amounts of labeled data.