Bayesian Evidential Learning for Few-Shot Classification

• URL: http://arxiv.org/abs/2207.13137v2
• Date: Wed, 4 Sep 2024 07:41:41 GMT
• Title: Bayesian Evidential Learning for Few-Shot Classification
• Authors: Xiongkun Linghu, Yan Bai, Yihang Lou, Shengsen Wu, Jinze Li, Jianzhong He, Tao Bai,
• Abstract summary: Few-Shot Classification aims to generalize from base classes to novel classes given very limited labeled samples. State-of-the-art solutions involve learning to find a good metric and representation space to compute the distance between samples. Despite the promising accuracy performance, how to model uncertainty for metric-based FSC methods effectively is still a challenge.
• Score: 22.46281648187903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning. State-of-the-art solutions involve learning to find a good metric and representation space to compute the distance between samples. Despite the promising accuracy performance, how to model uncertainty for metric-based FSC methods effectively is still a challenge. To model uncertainty, We place a distribution over class probability based on the theory of evidence. As a result, uncertainty modeling and metric learning can be decoupled. To reduce the uncertainty of classification, we propose a Bayesian evidence fusion theorem. Given observed samples, the network learns to get posterior distribution parameters given the prior parameters produced by the pre-trained network. Detailed gradient analysis shows that our method provides a smooth optimization target and can capture the uncertainty. The proposed method is agnostic to metric learning strategies and can be implemented as a plug-and-play module. We integrate our method into several newest FSC methods and demonstrate the improved accuracy and uncertainty quantification on standard FSC benchmarks.

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