  In Open Set Domain Adaptation (OSDA), large amounts of target samples are
drawn from the implicit categories that never appear in the source domain. Due
to the lack of their specific belonging, existing methods indiscriminately
regard them as a single class unknown. We challenge this broadly-adopted
practice that may arouse unexpected detrimental effects because the decision
boundaries between the implicit categories have been fully ignored. Instead, we
propose Self-supervised Class-Discovering Adapter (SCDA) that attempts to
achieve OSDA by gradually discovering those implicit classes, then
incorporating them to restructure the classifier and update the domain-adaptive
features iteratively. SCDA performs two alternate steps to achieve implicit
class discovery and self-supervised OSDA, respectively. By jointly optimizing
for two tasks, SCDA achieves the state-of-the-art in OSDA and shows a
competitive performance to unearth the implicit target classes.
