# Discriminative Few-Shot Learning Based on Directional Statistics Source: [Discriminative Few-Shot Learning Based on Directional Statistics]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:36:37.771Z Authors: Junyoung Park, Subin Yi, Yongseok Choi, Dong-Yeon Cho, Jiwon Kim Published: 2019-06-05T04:34:10.000Z ## Abstract Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy. We propose a novel algorithm to generate class representatives for few-shot classification tasks. As a probabilistic model for learned features of inputs, we consider a mixture of von Mises-Fisher distributions which is known to be more expressive than Gaussian in a high dimensional space. Then, from a discriminative classifier perspective, we get a better class representative considering inter-class correlation which has not been addressed by conventional few-shot learning algorithms. We apply our method to \\emph{mini}ImageNet and \\emph{tiered}ImageNet datasets, and show that the proposed approach outperforms other comparable methods in few-shot classification tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()