  Existing self-supervised learning methods learn representation by means of
pretext tasks which are either (1) discriminating that explicitly specify which
features should be separated or (2) aligning that precisely indicate which
features should be closed together, but ignore the fact how to jointly and
principally define which features to be repelled and which ones to be
attracted. In this work, we combine the positive aspects of the discriminating
and aligning methods, and design a hybrid method that addresses the above
issue. Our method explicitly specifies the repulsion and attraction mechanism
respectively by discriminative predictive task and concurrently maximizing
mutual information between paired views sharing redundant information. We
qualitatively and quantitatively show that our proposed model learns better
features that are more effective for the diverse downstream tasks ranging from
classification to semantic segmentation. Our experiments on nine established
benchmarks show that the proposed model consistently outperforms the existing
state-of-the-art results of self-supervised and transfer learning protocol.
