1. **Limited discussion of related works** on heterogeneous architectures and PCA-based methods. Below are some relevant examples. The authors are encouraged to conduct a more thorough literature review. Without a clear differentiation from existing methods, there is a concern about the novelty of the paper.
- Liu, Yufan, et al. "Cross-architecture knowledge distillation." *ACCV 2022*.
- Hofstätter, Sebastian, et al. "Improving efficient neural ranking models with cross-architecture knowledge distillation." *arXiv:2010.02666* (2020).
- Ni, Jianyuan, et al. "Adaptive Cross-Architecture Mutual Knowledge Distillation." *FG 2024*.
- Chiu, Tai-Yin, and Danna Gurari. "PCA-based knowledge distillation towards lightweight and content-style balanced photorealistic style transfer models." *CVPR 2022*.
- Guo, Yi, et al. "RdimKD: Generic Distillation Paradigm by Dimensionality Reduction." *arXiv:2312.08700* (2023).
2. **Unclear necessity and effectiveness of using CKA** for modularization. An ablation study comparing CKA-based modularization with simpler approaches, such as dividing networks based on feature map resolution, should be included.
3. **Writing quality could be improved** to enhance rigor and clarity. For example:
- In lines 56-57, the phrase “it begins by training … representative features are transferred first” is unclear. It is not intuitive why training the shallowest module would ensure the most representative features.
- There may be no need to distinguish between the two types of representation distances $d_{SM}$ and $d_{DM}$, as they are calculated in the same way.
- There may be no need to distinguish between the two types of representation distances $d_{SM}$ and $d_{DM}$, as they are calculated in the same way.