  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.
