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

Published: 09 Nov 2021, Last Modified: 08 Sept 2024NeurIPS 2021 PosterReaders: Everyone
Keywords: few-shot learning
TL;DR: We leverage samples from distractor classes or randomly generated noise to improve the generalization of few-shot learner
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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Supplementary Material: pdf
Code: https://github.com/VinAIResearch/poodle
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/poodle-improving-few-shot-learning-via/code)
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