WSEAS Transactions on Systems and Control
Print ISSN: 1991-8763, E-ISSN: 2224-2856
Volume 13, 2018
Data Augmentation and Transfer Learning for Limited Dataset Ship Classification
Authors: , , ,
Abstract: Fine-grained classification consists of learning and understanding the subtle details between visually similar classes, which is a difficult task even for a human expert trained in a corresponding scientific field. Similar performances can be achieved by deep learning algorithms, but this requires a great amount of data in the learning phase. Obtaining data samples and manual data labeling can be time-consuming and expensive. This is why it can be difficult to acquire the required amount of data in real conditions in many areas of application, so in the context of a limited dataset it is necessary to use other techniques, such as data augmentation and transfer learning. In this we paper we study the problem of fine-grained ship type classification with a dataset size which does not allow learning network from scratch. We will show that good classification accuracy can be achieved by artificially creating additional learning examples and by using pre-trained models which allow a transfer of knowledge between related source and target domains. In this, the source and target domain can differ in their entirety.
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Keywords: Deep Learning, Convolutional Neural Networks, Transfer Learning, Data Augmentation, Fine-grained Classification
Pages: 460-465
WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 13, 2018, Art. #50