GAN-Based Domain Adaptation for Object Classification

Recent trends in image classification focus on training deep neural networks that require having a large amount of training images related to the considered task. However, obtaining enough labeled image samples is often time-consuming and expensive. An alternative solution proposed is to transfer the knowledge learned while solving one problem to another but related problem, also called transfer learning. Domain adaptation is a type of transfer learning that deals with learning a model that performs well on two datasets that have different (but somehow correlated) data distributions. In this work, we present a new domain adaptation method based on generative adversarial networks (GANs) in the context of aerial image classification. Experimental results obtained on two datasets for a single object scenario show that the proposed method is particularly promising.