  In open-set recognition, existing methods generally learn statically fixed
decision boundaries using known classes to reject unknown classes. Though they
have achieved promising results, such decision boundaries are evidently
insufficient for universal unknown classes in dynamic and open scenarios as
they can potentially appear at any position in the feature space. Moreover,
these methods just simply reject unknown class samples during testing without
any effective utilization for them. In fact, such samples completely can
constitute the true instantiated representation of the unknown classes to
further enhance the model's performance. To address these issues, this paper
proposes a novel dynamic against dynamic idea, i.e., dynamic method against
dynamic changing open-set world, where an open-set self-learning (OSSL)
framework is correspondingly developed. OSSL starts with a good closed-set
classifier trained by known classes and utilizes available test samples for
model adaptation during testing, thus gaining the adaptability to changing data
distributions. In particular, a novel self-matching module is designed for
OSSL, which can achieve the adaptation in automatically identifying known class
samples while rejecting unknown class samples which are further utilized to
enhance the discriminability of the model as the instantiated representation of
unknown classes. Our method establishes new performance milestones respectively
in almost all standard and cross-data benchmarks.
