Algorithm 1: Pseudocode of PIC in a PyTorch-likestyle

LinearEvaluationProtocol Inlinear evaluation, wefollowthecommon setting [6,5]tofreeze the backbone of ResNet-50 and train a supervised linear classifier on the global average pooling features for100 epochs. Note that, the2-layer head inunsupervised pre-training isnotused inthe linear evaluation stage. During training, we augment the image with random scaling from 0.5 to 2.0, crop size of 769 and random flip. The top-1 and top-5 accuracyresults are reported inTable9. From the perspective of optimization goals, the only difference between the parametric instance classification framework and supervised classification framework is how to define the classes for each instance.