Abstract

Semi-supervised learning is adopted for fine-grained image classification with convolutional networks. Compared with the traditional approach of distillation, we obtain accuracy improvement of ~3 percent points under the upper limit of supervised learning on cassava-disease dataset.

© 2019 The Author(s)

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