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Compare Learning: Bi-Attention Network For Few-Shot Learning

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IEEE ICASSP 2020 Virtual Conference May 2020

Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep dista

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Compare Learning: Bi-Attention Network For Few-Shot Learning

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Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep dista