ReMarNet: conjoint relation and margin learning for small-sample image classification

Li, X., Yu, L., Yang, X. , Ma, Z., Xue, J.-H., Cao, J. and Guo, J. (2020) ReMarNet: conjoint relation and margin learning for small-sample image classification. IEEE Transactions on Circuits and Systems for Video Technology, (doi: 10.1109/TCSVT.2020.3005807) (Early Online Publication)

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Abstract

Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small. To ensure good generalizability of deep networks under small sample sizes, learning discriminative features is crucial. To this end, several loss functions have been proposed to encourage large intra-class compactness and inter-class separability. In this paper, we propose to enhance the discriminative power of features from a new perspective by introducing a novel neural network termed Relation-and- Margin learning Network (ReMarNet). Our method assembles two networks of different backbones so as to learn the features that can perform excellently in both of the aforementioned two classification mechanisms. Specifically, a relation network is used to learn the features that can support classification based on the similarity between a sample and a class prototype; at the meantime, a fully connected network with the cross entropy loss is used for classification via the decision boundary. Experiments on four image datasets demonstrate that our approach is effective in learning discriminative features from a small set of labeled samples and achieves competitive performance against state-of-the-art methods. Code is available at https://github.com/liyunyu08/ReMarNet.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yang, Ms Xiaochen
Authors: Li, X., Yu, L., Yang, X., Ma, Z., Xue, J.-H., Cao, J., and Guo, J.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Transactions on Circuits and Systems for Video Technology
Publisher:IEEE
ISSN:1051-8215
ISSN (Online):1558-2205
Published Online:29 June 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE Transactions on Circuits and Systems for Video Technology 2020
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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