Parametric Instance Classification for Unsupervised Visual Feature Learning

Understand

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.