MetAdapt: Meta-learned task-adaptive architecture for few-shot classification

Sivan Doveh*, Eli Schwartz, Chao Xue, Rogerio Feris, Alex Bronstein, Raja Giryes, Leonid Karlinsky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Recently, great progress has been made in the field of Few-Shot Learning (FSL). While many different methods have been proposed, one of the key factors leading to higher FSL performance is surprisingly simple. It is the backbone network architecture used to embed the images of the few-shot tasks. While first works on FSL resorted to small architectures with just a few convolution layers, recent works show that large architectures pre-trained on the training portion of FSL datasets produce strong features that are more easily transferable to novel few-shot tasks, thus attaining significant gains to methods using them. Despite these observations, little to no work has been done towards finding the right backbone for FSL. In this paper we propose MetAdapt that not only meta-searches for an optimized architecture for FSL using Network Architecture Search (NAS), but also results in a model that can adaptively ‘re-wire’ itself predicting the better architecture for a given novel few-shot task. Using the proposed approach we observe strong results on two popular few-shot benchmarks: miniImageNet and FC100.

Original languageEnglish
Pages (from-to)130-136
Number of pages7
JournalPattern Recognition Letters
StatePublished - Sep 2021


FundersFunder number
Defense Advanced Research Projects AgencyFA8750-19-C-1001


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