Summary of Domain Generalization Through Meta-learning: a Survey, by Arsham Gholamzadeh Khoee et al.
Domain Generalization through Meta-Learning: A Survey
by Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 addresses the limitation of deep neural networks (DNNs) in handling out-of-distribution (OOD) data, a common scenario in real-world applications due to domain shifts. DNNs often overfit and generalize poorly across tasks and domains despite their effectiveness with large datasets and computational power. Meta-learning is proposed as a promising approach to acquire transferable knowledge across tasks for fast adaptation, eliminating the need to learn each task from scratch. The paper delves into meta-learning for domain generalization, introducing a novel taxonomy based on feature extraction strategy and classifier learning methodology. A decision graph is presented to help readers navigate the taxonomy based on data availability and domain shifts. The survey provides practical insights and discusses promising research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how deep neural networks (DNNs) have a problem when they’re faced with new data that’s different from what they were trained on. This is a big deal because in real life, data can change or be different in important ways. The paper looks at a way to solve this problem called meta-learning. Meta-learning helps machines learn how to adapt quickly to new situations without needing a lot of training data. The paper also explains a new way to organize the different approaches to meta-learning and provides some guidance on which one to use depending on the situation. |
Keywords
* Artificial intelligence * Domain generalization * Feature extraction * Meta learning
