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Semi-Supervised Few-Shot Learning with Prototypical Random Walks
AAAI Workshop on Meta-Learning and MetaDL Challenge, PMLR 140:45-57, 2021.
Abstract
Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach, dubbed as Prototypical Random Walk Networks(PRWN), built on top of Prototypical Networks (PN). We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated. Our work is related to the very recent development of graph-based approaches for few-shot learning. However, we show that compact and well-separated class representations can be achieved by modeling our prototypical random walk notion without needing additional graph-NN parameters or requiring a transductive setting where a collective test set is provided. Our model outperforms baselines in most benchmarks with significant improvements in some cases. Our model, trained with 40 of the data as labeled, compares competitively against fully supervised prototypical networks, trained on 100 of the labels, even outperforming it in the 1-shot mini-Imagenet case with 50.89 to 49.4 accuracy. We also show that our loss is resistant to distractors, unlabeled data that does not belong to any of the training classes, and hence reflecting robustness to labeled/unlabeled class distribution mismatch.