Summary of Perturbing the Gradient For Alleviating Meta Overfitting, by Manas Gogoi et al.
Perturbing the Gradient for Alleviating Meta Overfitting
by Manas Gogoi, Sambhavi Tiwari, Shekhar Verma
First submitted to arxiv on: 20 May 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 proposed solutions aim to address Meta Overfitting in few-shot learning settings by increasing diversity in tasks and reducing model confidence. The issue arises from Mutual Non-exclusivity and Lack of diversity, causing a single global function to fit support set data but fail to generalize to new tasks. Novel approaches include few-shot sinusoid regression and classification, demonstrating improved generalization performance compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve the problem of Meta Overfitting in machine learning. It happens when a model is very good at fitting the data it’s trained on, but then doesn’t do well with new, unseen data. The reason for this is that there isn’t enough variety in the training tasks. Some solutions proposed include making sure there are more different types of tasks and making the model less confident about its predictions. |
Keywords
» Artificial intelligence » Classification » Few shot » Generalization » Machine learning » Overfitting » Regression
