Cycle optimization metric learning for few-shot classification. (July 2023)

• Record Type: Journal Article
• Authors: Liu, Qifan; Cao, Wenming; He, Zhihai
• Is Part Of: Pattern recognition. Volume 139(2023)
• Publication Date: 2023-07
• Subjects: 00-01 -- 99-00; Few-shot learning -- Cycle optimization -- Image classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109468
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We explore the commutative relationship between support and query samples and design a novel cycle optimization metric network for few-shot classification tasks. We construct a cycle consistency loss in the mutual prediction process of support and query samples. The Forward network provides the prediction from support set to query set, then the Reverse network conducts the backward prediction. The Forward-Reverse network framework can achieve better performance under the cycle consistency loss. We also introduce an optimization module, which can help correct the predicted results via an optimization loss to further improve the performance of our model. Our method outperforms existing few-shot learning methods on various datasets. Abstract: Metric learning methods are widely used in few-shot learning due to their simplicity and effectiveness. Most existing methods directly predict query labels by comparing the similarity between support and query samples. In this paper, we design a cycle optimization metric network for few-shot classification task that optimizes model performance based on loop-prediction of the labels of query samples and support samples. Specifically, we construct a forward network and reverse network based on a geometric algebra Graph Neural Network (GA-GNN). These two networks form the loop prediction from support samples to query samples and then back to support samples, guided by a cycle-consistency loss. We also introduce an optimization module that is able to correct the predicted results of query samples to further improve the network performance. Our extensive experimental results demonstrate that the proposed cycle optimization metric network outperforms existing state-of-the-art few-shot learning methods on classification tasks.