Appendices: Contextually Affinitive Neighborhood Refinery for Deep Clustering A More Experimental Results A.1 Training Efficiency

We show the training efficiency of ConNR by comparing its training speed with a standard efficient SSL baseline BYOL. ConAff neighborhood can be injected into the group-aware concordance loss. Tiny-ImageNet which consists of 200 classes with 10,0000 training images in total. Table 5 indicate that our approach can successfully scale to large datasets. These outcomes demonstrate the effectiveness and scalability of our proposed method when applied to Tiny-ImageNet.