Transfer learning is crucial in medical imaging due to data scarcity. MedMerge proposes merging models from different initializations to boost performance. The method learns kernel-level weights for effective model merging. Testing on various tasks shows up to a 3% improvement in F1 score. Batch normalization plays a critical role in model merging. Results indicate that MedMerge outperforms traditional fine-tuning methods and linear probing. Computational cost is reduced by using kernel-level weights instead of parameter-level weights. The importance of batch normalization layers during the merging process is highlighted. Combining features learned from different tasks can significantly improve model performance.
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by Ibrahim Alma... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11646.pdfDeeper Inquiries