1.This paper is challenging to follow, and the proposed method is highly complex, making it difficult to reproduce.
2.The proposed method comprises several complicated modules and has more parameters than the baselines. It remains unclear whether the main performance gain originates from a particular module or if the improvement is merely due to having more parameters. The current version of the ablation study does not provide definitive answers to these questions.
3.The authors claim that one of their main contributions is the use of a Mahalanobis contrastive learning method to narrow the distribution gap between retrieved examples and current samples. However, there are no experiments to verify whether Mahalanobis yields better results than standard contrastive learning.
4.The proposed method involves multiple modules, which could impact training and inference speed. There should be experiments conducted to study and analyze these effects.
2.The proposed method comprises several complicated modules and has more parameters than the baselines. It remains unclear whether the main performance gain originates from a particular module or if the improvement is merely due to having more parameters. The current version of the ablation study does not provide definitive answers to these questions.