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

• URL: http://arxiv.org/abs/2206.04679v1
• Date: Wed, 8 Jun 2022 18:59:21 GMT
• Title: POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
• Authors: Duong H. Le, Khoi D. Nguyen, Khoi Nguyen, Quoc-Huy Tran, Rang Nguyen, Binh-Son Hua
• Abstract summary: We propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. 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.
• Score: 19.311470287767385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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