Summary of Enhancing Deep Learning Model Robustness Through Metamorphic Re-training, by Said Togru et al.
Enhancing Deep Learning Model Robustness through Metamorphic Re-Training
by Said Togru, Youssef Sameh Mostafa, Karim Lotfy
First submitted to arxiv on: 2 Dec 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 paper proposes a Metamorphic Retraining Framework that leverages semi-supervised learning algorithms and metamorphic relations to enhance the robustness and real-world performance of machine learning models. The framework integrates multiple retraining algorithms, including FixMatch, FlexMatch, MixMatch, and FullMatch, to automate model retraining, evaluation, and testing with specified configurations. Experiments on CIFAR-10, CIFAR-100, and MNIST datasets using various image processing models, both pretrained and non-pretrained, demonstrate the framework’s potential to significantly improve model robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how using metamorphic relations can make machine learning models more robust and better at handling real-world data. They created a special kind of training process that uses multiple algorithms to adapt models to new situations. This helps models work better in different environments and reduces the chance of them making mistakes. The researchers tested their approach on several popular datasets and found it worked really well, increasing model performance by an average of 17 percent. |
Keywords
» Artificial intelligence » Machine learning » Semi supervised
