2020/09/19 by Zhang, Youshan, Davison, Brian D. · 1 citation
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2009.09289
Domain adaptation is one of the most crucial techniques to mitigate the domain shift problem, which exists when transferring knowledge from an abundant labeled sourced domain to a target domain with few or no labels. Partial domain adaptation addresses the scenario when target categories are only a subset of source categories. In this paper, to enable the efficient representation of cross-domain plant images, we first extract deep features from pre-trained models and then develop adversarial consistent learning (ACL) in a unified deep architecture for partial domain adaptation. It consists of source domain classification loss, adversarial learning loss, and feature consistency loss. Adversarial learning loss can maintain domain-invariant features between the source and target domains. Moreover, feature consistency loss can preserve the fine-grained feature transition between two domains. We also find the shared categories of two domains via down-weighting the irrelevant categories in the source domain. Experimental results demonstrate that training features from NASNetLarge model with proposed ACL architecture yields promising results on the PlantCLEF 2020 Challenge.