Strategies for Meta-Learning with Diverse TasksDownload PDF

Published: 09 May 2022, Last Modified: 12 May 2023MIDL 2022 Short PapersReaders: Everyone
Keywords: Meta-Learning
Abstract: A major limitation of deep learning for medical applications is the scarcity of labelled data. Meta-learning, which leverages principles learned from previous tasks for new tasks, has the potential to mitigate this data scarcity. However, most meta-learning methods assume idealised settings with homogeneous task definitions. The most widely used family of meta-learning methods, those based on Model-Agnostic Meta-Learning (MAML), require a constant network architecture and therefore a fixed number of classes per classification task. Here, we extend MAML to more realistic settings in which the number of classes can vary by adding a new classification layer for each new task. Specifically, we investigate various initialisation strategies for these new layers. We identify a number of such strategies that substantially outperform the naive default (Kaiming) initialisation scheme.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Meta Learning
Secondary Subject Area: Detection and Diagnosis
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TL;DR: We explore different head initialization strategies for a gradient based meta-learning method for scenarios with variable number of target labels.
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