Accuracy Improvement for Fine-Grained Image Classification with Semi-Supervised Learning

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.