Contextual Gradient Scaling for Few-Shot Learning

• URL: http://arxiv.org/abs/2110.10353v1
• Date: Wed, 20 Oct 2021 03:05:58 GMT
• Title: Contextual Gradient Scaling for Few-Shot Learning
• Authors: Sanghyuk Lee, Seunghyun Lee, Byung Cheol Song
• Abstract summary: We propose contextual gradient scaling (CxGrad) for model-agnostic meta-learning (MAML) CxGrad scales gradient norms of the backbone to facilitate learning task-specific knowledge in the inner-loop. Experimental results show that CxGrad effectively encourages the backbone to learn task-specific knowledge in the inner-loop.
• Score: 24.19934081878197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 high-level layers are small than those of the other layers. So, the backbone of MAML usually learns task-generic features, which results in deteriorated adaptation performance in the inner-loop. To resolve or mitigate this problem, we propose contextual gradient scaling (CxGrad), which scales gradient norms of the backbone to facilitate learning task-specific knowledge in the inner-loop. Since the scaling factors are generated from task-conditioned parameters, gradient norms of the backbone can be scaled in a task-wise fashion. Experimental results show that CxGrad effectively encourages the backbone to learn task-specific knowledge in the inner-loop and improves the performance of MAML up to a significant margin in both same- and cross-domain few-shot classification.

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