Multi-staged deep learning with created coarse and appended fine categories

This paper proposes a new learning method for Deep Learning based on the concept of a Coarse-to-Fine approach. The Coarse-to-Fine classification improves Deep Learning performance, but it increases network size and presents the problem of close dependence on the accuracy of coarse classification. We tried to avoid this problem by adopting the concept of Curriculum Learning and succeeded in improving the accuracy of Deep Learning. This technique uses learning that employs a single closed image dataset several times in the same network except for the last layer. In this process, coarse labels are given to the images during the pre-training stages and fine labels are given to the same images at the fine-tuning stage. This coarse category pre-training method makes it possible to obtain those features that commonly exist in multiple fine categories. To demonstrate the advantage of this technique, several patterns of a dataset in the quantity of several tens of classes and a single dataset of 100 classes were produced using the ImageNet dataset and compared with the previous technique. The results showed a 5.7% improvement of TOP1 accuracy, with the best case confirmed in the 100-class dataset.