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arXiv · 2206.04679

POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples

Duong H. Le·Khoi D. Nguyen·Khoi Nguyen·Quoc-Huy Tran·Rang Nguyen·Binh-Son Hua
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Abstract

In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Specifically, we exploit the easily available out-of-distribution samples to drive the classifier to avoid irrelevant features by maximizing the distance from prototypes to out-of-distribution samples while minimizing that of in-distribution samples (i.e., support, query data). Our approach is simple to implement, agnostic to feature extractors, lightweight without any additional cost for pre-training, and applicable to both inductive and transductive settings. Extensive experiments on various standard benchmarks demonstrate that the proposed method consistently improves the performance of pretrained networks with different architectures.

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Duong H. Le, Khoi D. Nguyen, Khoi Nguyen, Quoc-Huy Tran, Rang Nguyen, Binh-Son Hua. 2022-06-08. POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples. https://arxiv.org/abs/2206.04679

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