Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
This work addresses the problem of few-shot learning for AI systems by offering a simpler and more effective approach than previous methods, though it appears incremental.
The paper tackles few-shot classification by proposing a feature selection strategy from a multi-domain representation, achieving state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.