Contextual Gradient Scaling for Few-Shot Learning

@article{Lee2021ContextualGS,
  title={Contextual Gradient Scaling for Few-Shot Learning},
  author={Sanghyuk Lee and Seunghyun Lee and Byung Cheol Song},
  journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={3503-3512}
}
Model-agnostic meta-learning (MAML) is a well-known optimization-based meta-learning algorithm that works well in various computer vision tasks, e.g., few-shot classification. MAML is to learn an initialization so that a model can adapt to a new task in a few steps. However, since the gradient norm of a classifier (head) is much bigger than those of backbone layers, the model focuses on learning the decision boundary of the classifier with similar representations. Furthermore, gradient norms of… 

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