Distributionally Robust k-Nearest Neighbors for Few-Shot Learning
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Learning a robust classifier from a few samples remains a key challenge in machine learning. A major thrust of research in few-shot classification has been based on metric learning to capture similarities between samples and then perform the k-nearest neighbor algorithm. To make such an algorithm more robust, in this paper, we propose a distributionally robust k-nearest neighbor algorithm Dr.k-NN, which features assigning minimax optimal weights to training samples when performing classification. We also couple it with neural-network-based feature embedding. We demonstrate the competitive performance of our algorithm comparing to the state-of-the-art in the few-shot learning setting with various real-data experiments.
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