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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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Wednesday 6th July
Poster Session 1.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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