Curriculum based Dropout Discriminator for Domain Adaptation

Visual recognition has seen vast improvements based mainly on the success of deep learning based models [17]. These models are trained on very large annotated datasets such as Imagenet [35]. The deployment of these generically trained models require them to adapt to work in specific settings (for instance with catalog images in E-commerce websites). This problem is recognized as one of dataset bias and was demonstrated through the work of [48]. However, the requirement of a large annotated dataset becomes a bottleneck for training networks in deep learning frameworks. In this paper, we tackle the problem of adapting classifiers to work on datasets that do not have any labeled information. This problem is one of unsupervised domain adaptation in a more general setting. Ganin and Lempitsky [11] proposed a method to solve unsupervised domain adaptation through back-propagation. In this method, the domain adaptation problem is solved by using a discriminator that ensures domain invariance of learned representations used for classification.