Summary of Fissionfusion: Fast Geometric Generation and Hierarchical Souping For Medical Image Analysis, by Santosh Sanjeev et al.
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
by Santosh Sanjeev, Nuren Zhaksylyk, Ibrahim Almakky, Anees Ur Rehman Hashmi, Mohammad Areeb Qazi, Mohammad Yaqub
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hierarchical merging approach, which combines local and global aggregation of models at various levels based on their hyperparameter configurations, demonstrates significant improvements over the model souping approach across multiple datasets while maintaining low computational costs. The method uses a cyclical learning rate scheduler to produce multiple models for aggregation in the weight space, alleviating the need for training a large number of models in the hyperparameter search. By leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP, and fine-tuning these models on medical imaging tasks, the approach achieves better results on out-of-distribution datasets compared to model soups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method for improving medical image classification by combining multiple pre-trained models. This is done using a hierarchical merging approach that aggregates models at different levels based on their hyperparameters. The authors also introduce an efficient way to generate multiple models without training them all from scratch. The results show that this approach performs better than previous methods, especially when classifying images outside of the original training set. |
Keywords
» Artificial intelligence » Fine tuning » Hyperparameter » Image classification » Transfer learning
