Constrained-HIDA: Heterogeneous Image Domain Adaptation Guided by Constraints

Abstract

Supervised deep learning relies heavily on the existence of a huge amount of labelled data, which in many cases is difficult to obtain. Domain adaptation deals with this problem by learning on a labelled dataset and applying that knowledge to another, unlabelled or scarcely labelled dataset, with a related but different probability distribution. Heterogeneous domain adaptation is an especially challenging area where domains lie in different input spaces. These methods are very interesting for the field of remote sensing (and indeed computer vision in general), where a variety of sensors are used, capturing images of different modalities, different spatial and spectral resolutions, and where labelling is a very expensive process. With two heterogeneous domains, however, unsupervised domain adaptation is difficult to perform, and class-flipping is frequent. At least a small amount of labelled data is therefore necessary in the target domain in many cases. This work proposes loosening the label requirement by labelling the target domain with must-link and cannot-link constraints instead of class labels. Our method Constrained-HIDA, based on constraints, contrastive loss, and learning domain invariant features, shows that a significant performance improvement can be achieved by using a very small number of constraints. This demonstrates that a reduced amount of information, in the form of constraints, is as effective as giving class labels. Moreover, this paper shows the benefits of interactive supervision—assigning constraints to the samples from classes that are known to be prone to flipping can further reduce the necessary amount of constraints.