MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities
Authors: Kunxi Li, Tianyu Zhan, Kairui Fu, Shengyu Zhang, Kun Kuang, Jiwei Li, Zhou Zhao, Fei Wu


Reviewer paper summaries: This paper introduces MergeNet, a novel framework designed to facilitate knowledge transfer across heterogeneous models, tasks, and modalities. MergeNet allows for dynamic mapping and adaptation of low-rank parameters from a source model to a target model, enabling effective knowledge transfer even between models with different architectures and tasks. The paper demonstrates the practical applications of MergeNet in enhancing the performance of smaller models on edge devices by leveraging knowledge from larger models, without increasing their computational complexity.
Summary of strengths: - Novel methods: While the employment of LoRA seems to be boring but the proposal of LPKA is quite interesting. Would like to see why only separate them in row, column (I suppose it is width and height) but not in channel.
    
- Experimental results are good. MergeNet has the best performance on different proposed tasks.
Summary of weaknesses: 1. Where are the results of Table 1 on MergeNet (M->R)
	1. Which layers you choose?
2. What is mutual knowledge transfer? 
	1. For MergeNet (R<->M), does it mean that authors train ResNet50 and MobileNetV2 before the knowledge transfer on Cifar10?
3. What is the loss for MergeNet? 
	1. Do authors use ground truth?
4. What is the setting of Sec 4.2 cross-modal knowledge transfer? How is the transfer conduct? What is the loss function? Which layers you choose?
	1. Is the model already trained on MSCOCO or VQAv2.0? 
5. What is the mutual knowledge transfer results for Tab. 2 and Tab. 3?
6. What is the setting of Sec. 4.5. What is the pretrained dataset of ResNet50?

Overall, this paper is quite far from the standard of *ACL as there are so many details missing, which are important to validate and reproduce the results. I suggest authors proofread this paper and resubmit.

Also, the majority of experiments are conducted on Vision while *ACL focuses on NLP, which might be not that aligned.
Comments, suggestions and typos: More details pls.
Overall assessment scores: 2