  This paper presents parametric instance classification (PIC) for unsupervised
visual feature learning. Unlike the state-of-the-art approaches which do
instance discrimination in a dual-branch non-parametric fashion, PIC directly
performs a one-branch parametric instance classification, revealing a simple
framework similar to supervised classification and without the need to address
the information leakage issue. We show that the simple PIC framework can be as
effective as the state-of-the-art approaches, i.e. SimCLR and MoCo v2, by
adapting several common component settings used in the state-of-the-art
approaches. We also propose two novel techniques to further improve
effectiveness and practicality of PIC: 1) a sliding-window data scheduler,
instead of the previous epoch-based data scheduler, which addresses the
extremely infrequent instance visiting issue in PIC and improves the
effectiveness; 2) a negative sampling and weight update correction approach to
reduce the training time and GPU memory consumption, which also enables
application of PIC to almost unlimited training images. We hope that the PIC
framework can serve as a simple baseline to facilitate future study.
