POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
This addresses the challenge of limited labeled data in few-shot learning for machine learning practitioners, though it appears incremental as it builds on existing pretrained networks.
The paper tackles the problem of few-shot learning by using out-of-distribution samples to improve classifier performance, resulting in consistent performance gains across various benchmarks.
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.