Loss function: The proposed Decoupled Uniformity loss extends the uniformity loss in [54] and does not explicitly include the alignment loss in [54], which is elegant. The authors claimed that the proposed loss function can implicitly encourage alignment (also showed theoretically in Theorem 1 with insufficient training samples). The reviewer wonder what if you add the alignment loss similar to [54]. Would that increase or degrade the performance in practice, ie., when the number of the training samples is neither too low nor infinity? As shown in the experiment, without the prior information, the proposed method cannot beat the SOTA.
Assumptions: The authors introduced the Weak-aligned encoder, which is claimed to be weaker than previous assumptions such as L-smoothness. In the implementation, how to ensure this assumption is satisfied?
Assumptions: How to ensure Assumption 2 in implementation?
Definition 3.7: How to find the value of \lambda? Experiments:
. From the experimental results, without the prior information (the same as the benchmarks) the proposed method has no advantage compared to the SOTA. The advantage only shows when using the prior knowledge. Such comparison is a bit unfair, because in this case the proposed method essentially requires two representation models learned based on each dataset, ie., VAE/GAN + CL. Such extra complexity and cost need to be considered.
. Kernel quality is crucial to the downstream performance as shown experimentally in Appendix Fig. 4. In the implementation, how to choose a proper kernel with optimal parameters? Are these parameters jointly learned or picked via the validation sets?
. In Table 2, the performance of the proposed method is shown with 4 views. Do the benchmarks also use 4 views? If not, please provide results with 2 views for the fair comparison.
. From the experimental results, without the prior information (the same as the benchmarks) the proposed method has no advantage compared to the SOTA. The advantage only shows when using the prior knowledge. Such comparison is a bit unfair, because in this case the proposed method essentially requires two representation models learned based on each dataset, ie., VAE/GAN + CL. Such extra complexity and cost need to be considered.