Self-supervision of Feature Transformation for Further Improving Supervised Learning
This work addresses training efficiency for machine learning practitioners by offering an incremental improvement over existing self-supervised methods.
The paper tackles the problem of high training overhead in self-supervised learning by proposing a feature-based pretext task that discards regions of features to distinguish them, resulting in accuracy improvements across various supervised learning tasks.
Self-supervised learning, which benefits from automatically constructing labels through pre-designed pretext task, has recently been applied for strengthen supervised learning. Since previous self-supervised pretext tasks are based on input, they may incur huge additional training overhead. In this paper we find that features in CNNs can be also used for self-supervision. Thus we creatively design the \emph{feature-based pretext task} which requires only a small amount of additional training overhead. In our task we discard different particular regions of features, and then train the model to distinguish these different features. In order to fully apply our feature-based pretext task in supervised learning, we also propose a novel learning framework containing multi-classifiers for further improvement. Original labels will be expanded to joint labels via self-supervision of feature transformations. With more semantic information provided by our self-supervised tasks, this approach can train CNNs more effectively. Extensive experiments on various supervised learning tasks demonstrate the accuracy improvement and wide applicability of our method.