  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.
