Probabilistic Adaptation for Meta-Learning
Tameem Adel · 2023
Meta-learning models learn to generalise to unseen tasks at test time.We introduce a meta-learning algorithm which balances (global) generalisation with a (local) adaptive mechanism allowing the meta-learner to deal with potentially substantial heterogeneity in the task distribution.The proposed meta-learner flexibly consolidates shared components (responsible for generalisation) with task-specific components.The latter components are adapted, in a data-driven manner, based on estimating the similarity between the meta-test task in hand and the training tasks.Experiments demonstrate improved performance on few-shot learning benchmarks, both general and others involving a more heterogeneous set of tasks.