Related papers: Improving Task Adaptation for Cross-domain Few-shot Learning

Improving Task Adaptation for Cross-domain Few-shot Learning

URL: http://arxiv.org/abs/2107.00358v1
Date: Thu, 1 Jul 2021 10:47:06 GMT
Title: Improving Task Adaptation for Cross-domain Few-shot Learning
Authors: Wei-Hong Li, Xialei Liu, Hakan Bilen
Abstract summary: Cross-domain few-shot classification aims to learn a classifier from previously unseen classes and domains with few labeled samples. We show that parametric adapters attached to convolutional layers with residual connections performs the best.
Score: 41.821234589075445
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. We study several strategies including various adapter topologies and operations in terms of their performance and efficiency that can be easily attached to existing methods with different meta-training strategies and adapt them for a given task during meta-test phase. We show that parametric adapters attached to convolutional layers with residual connections performs the best, and significantly improves the performance of the state-of-the-art models in the Meta-Dataset benchmark with minor additional cost. Our code will be available at https://github.com/VICO-UoE/URL.

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