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Attention-based label consistency for semi-supervised deep learning based image classification
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DOI：10.1016/j.neucom.2020.06.133.png)
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Abstract
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Semi-supervised deep learning, which aims to effectively use the available unlabeled data to aid the model in learning from labeled data, is a hot topic recently. To effectively employ the abundant unlabeled data and handle the imbalance in labeled data, we propose a novel attention-based label consistency (ALC) model for semi-supervised deep learning. The relationships between different samples are well exploited by the proposed scheme of channel and sample attention; meanwhile, the class estimations are required to be smooth for nearby unlabeled data. The proposed ALC is further extended to the imbal-anced case by developing a label-imbalance ALC model. We have implemented the proposed ALC model in the semi-supervised frameworks of P model and MeanTeacher, and the experimental results on four benchmark datasets, (e.g., Fashion-MNIST, CIFAR-10, SVHN, and ImageNet) clearly show the advantages of our proposed method. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Semi-supervised learning
Deep neural network
Attention mechanism
Imbalance classification
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