Although the paper gives detailed theoretical proof, the experiments are somewhat weak. I still have some concerns: 1）The most related works SwaV and Barlow Twins outperform the proposed method in some experimental results, as shown in Table 1,2,5. What are the main advantages of this method compared with SwaV and Barlow Twins? 2) HSIC(Z, Y) can be seen as a distance metric in the kernel space, where the cluster structure is defined by the identity. Although this paper maps identity labels into the kernel space, the information of one-hot label is somewhat limited compared with views embeddings in Barlow Twins. 3)Since the cluster structure is defined by the identity. How does the number of images impact the model performance? Do more training images make the performance worse or better ?
BYOL in the abstract should be explained for its first appearance.
3)Since the cluster structure is defined by the identity. How does the number of images impact the model performance? Do more training images make the performance worse or better ? BYOL in the abstract should be explained for its first appearance.