[TASKNORM: Rethinking Batch Normalization for Meta-Learning]
(J. Bronskill et al., ICML-2020)
Batch normalization is an essential component of modern deep learning.
Meta-learning also relies on deep neural networks, so batch normalization needs to be tailored, according to the hierarchical nature of meta-learning.
This paper begins with having a close look at various normalization methods, in the meta-learning framework.
Then it presents “TASKNORM” which normalizes a task with the context set moments in combination with a set of non-transductive secondary moments (instance normalization or layer normalization) computed from the input being normalized. Experiments demonstrate that TASKNORM consistently improves the performance.
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