DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images

Author:

Qazi Mohammad AreebORCID,Almakky IbrahimORCID,Hashmi Anees Ur RehmanORCID,Sanjeev SantoshORCID,Yaqub MohammadORCID

Publisher

Springer Nature Switzerland

Reference41 articles.

1. Ainsworth, S.K., Hayase, J., Srinivasa, S.: Git Re-Basin: merging models modulo permutation symmetries. arXiv preprint arXiv:2209.04836 (2022)

2. Almakky, I., Sanjeev, S., Hashmi, A.U.R., Qazi, M.A., Yaqub, M.: MedMerge: merging models for effective transfer learning to medical imaging tasks. arXiv preprint arXiv:2403.11646 (2024)

3. Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M.: Lung and colon cancer histopathological image dataset (lc25000) (2019)

4. Boschini, M., et al.: Transfer without forgetting. In: European Conference on Computer Vision, pp. 692–709. Springer (2022). https://doi.org/10.1007/978-3-031-20050-2_40

5. Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. Adv. Neural. Inf. Process. Syst. 33, 15920–15930 (2020)

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