1. The rationale behind this work stems from the observation that current parameter isolation methods often hinder the acquisition of new task knowledge. Thus, the authors suggest pathway protection based on the sparsity exhibited by activation channels in deep networks. However, some parameter isolation methods are specifically tailored to leverage this sparsity. The clarification on how the proposed method avoids impeding the learning of new task knowledge remains ambiguous.
2. The terms "pathway" and "channel" lack clarity. A precise definition of these concepts and a comparison with prior works such as Piggyback, Packnet, and MEAT [1] would enhance the understanding. The proposed method appears conceptually similar to these existing works.
3. The language used in the paper is perplexing. For instance, in the abstract: "Given the sparsity of activation channels in a deep network, we introduce a novel misaligned fusion method within the context of continuous learning." The meaning of "misaligned fusion" and its relevance is unclear.
4. Line #80~81: "Therefore, pathways protection is all you need." Such a statement lacks empirical support. It would be beneficial for the authors to provide more rigorous evidence beyond mere examples.
5. Line #239~241: "Graph matching bears resemblance to a quadratic assignment problem (QAP) (Loiola et al., 2007), with the objective of establishing correspondences between the nodes in an image and the edges connecting these nodes." The reference to "nodes in an image" requires clarification. Additionally, line #246: "In our framework, a deep network is conceptualized as an image." The authors offer no elucidation on how a deep network can be likened to an image.
Given the uncertainties raised, evaluating the work poses a challenge. The authors appear to lack sufficient writing and publishing experience. I will proceed with the review and reassess the work following a response from the authors.
### Reference:
[1] Meta-Attention for ViT-Backed Continual Learning, CVPR 2022.
1. The rationale behind this work stems from the observation that current parameter isolation methods often hinder the acquisition of new task knowledge. Thus, the authors suggest pathway protection based on the sparsity exhibited by activation channels in deep networks. However, some parameter isolation methods are specifically tailored to leverage this sparsity. The clarification on how the proposed method avoids impeding the learning of new task knowledge remains ambiguous.