Survey of visual recognition with deep learning on new domains using only a few examples
Survey of visual recognition with deep learning on new domains using only a few examples
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
arXiv paper abstract https://arxiv.org/abs/2303.08557
arXiv PDF paper https://arxiv.org/pdf/2303.08557.pdf
Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data.
To address this, Few-shot learning (FSL) is proposed, but it assumes that all samples (including source and target task data, where target tasks are performed with prior knowledge from source ones) are from the same domain, which is a stringent assumption in the real world.
To alleviate this limitation, Cross-domain few-shot learning (CDFSL) has gained attention as it allows source and target data from different domains and label spaces.
This paper provides a comprehensive review of CDFSL at the first time, which has received far less attention than FSL due to its unique setup and difficulties.
... This review first introduces the definition of CDFSL and the issues involved, followed by the core scientific question and challenge.
A comprehensive review of validated CDFSL approaches from the existing literature is then presented, along with their detailed descriptions based on a rigorous taxonomy ...
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