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Self-supervised learning, which benefits from automatically constructinglabels through pre-designed pretext task, has recently been applied forstrengthen supervised learning. Since previous self-supervised pretext tasksare based on input, they may incur huge additional training overhead. In thispaper we find that features in CNNs can be also used for self-supervision. Thuswe creatively design the \emph{feature-based pretext task} which requires onlya small amount of additional training overhead. In our task we discarddifferent particular regions of features, and then train the model todistinguish these different features. In order to fully apply our feature-basedpretext task in supervised learning, we also propose a novel learning frameworkcontaining multi-classifiers for further improvement. Original labels will beexpanded to joint labels via self-supervision of feature transformations. Withmore semantic information provided by our self-supervised tasks, this approachcan train CNNs more effectively. Extensive experiments on various supervisedlearning tasks demonstrate the accuracy improvement and wide applicability ofour method.

Self-supervision of Feature Transformation for Further Improving Supervised Learning