Curriculum Meta-Learning for Few-shot Classification

• URL: http://arxiv.org/abs/2112.02913v1
• Date: Mon, 6 Dec 2021 10:29:23 GMT
• Title: Curriculum Meta-Learning for Few-shot Classification
• Authors: Emmanouil Stergiadis, Priyanka Agrawal, Oliver Squire
• Abstract summary: We propose an adaptation of the curriculum training framework, applicable to state-of-the-art meta learning techniques for few-shot classification. Our experiments with the MAML algorithm on two few-shot image classification tasks show significant gains with the curriculum training framework.
• Score: 1.5039745292757671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an adaptation of the curriculum training framework, applicable to state-of-the-art meta learning techniques for few-shot classification. Curriculum-based training popularly attempts to mimic human learning by progressively increasing the training complexity to enable incremental concept learning. As the meta-learner's goal is learning how to learn from as few samples as possible, the exact number of those samples (i.e. the size of the support set) arises as a natural proxy of a given task's difficulty. We define a simple yet novel curriculum schedule that begins with a larger support size and progressively reduces it throughout training to eventually match the desired shot-size of the test setup. This proposed method boosts the learning efficiency as well as the generalization capability. Our experiments with the MAML algorithm on two few-shot image classification tasks show significant gains with the curriculum training framework. Ablation studies corroborate the independence of our proposed method from the model architecture as well as the meta-learning hyperparameters

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