Summary of The Effectiveness Of Random Forgetting For Robust Generalization, by Vijaya Raghavan T Ramkumar et al.
The Effectiveness of Random Forgetting for Robust Generalization
by Vijaya Raghavan T Ramkumar, Bahram Zonooz, Elahe Arani
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Forget to Mitigate Overfitting (FOMO), a novel learning paradigm that alternates between forgetting and relearning phases to improve the robustness of deep neural networks against adversarial attacks. FOMO is motivated by the concept of active forgetting in the brain, where it regulates the model’s information through weight reinitialization. The approach alleviates robust overfitting by reducing the gap between the best and last robust test accuracy while improving state-of-the-art robustness. Experiments on benchmark datasets and adversarial attacks show that FOMO outperforms baseline adversarial methods in terms of trade-off between standard and robust accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forget to Mitigate Overfitting (FOMO) is a new way to make deep neural networks better at dealing with fake or misleading information. It’s like how our brains forget some things so we can learn new ones, but for computers! FOMO helps prevent the problem of “overfitting” where the computer gets too good at recognizing one set of data and fails on other sets. This is important because sometimes bad guys try to trick computers by making fake data. By using FOMO, scientists can make computers more robust and accurate in many real-world scenarios. |
Keywords
* Artificial intelligence * Overfitting
