Unsupervised domain adaptation with similarity learning for images

摘要

Systems and methods for addressing the cross domain issue using a similarity based classifier convolutional neural network. An input image is passed through a convolutional neural network that extracts its features. These features are compared to features of multiple sets of prototype representations with each set of prototype representations being extracted from and representing a category of images. The similarity between the features of the input image and features of the various prototype representations is scored and the prototype representation whose features are most similar to the features of the input image will have its label applied to the input image. The classifier is trained using images from a source domain and the input images are from a target domain. The training for the classifier is such that the classifier will be unable to determine if a specific data point is from the source domain or from the target domain.