  Traditional classifiers are deployed under closed-set setting, with both
training and test classes belong to the same set. However, real-world
applications probably face the input of unknown categories, and the model will
recognize them as known ones. Under such circumstances, open-set recognition is
proposed to maintain classification performance on known classes and reject
unknowns. The closed-set models make overconfident predictions over familiar
known class instances, so that calibration and thresholding across categories
become essential issues when extending to an open-set environment. To this end,
we proposed to learn PlaceholdeRs for Open-SEt Recognition (Proser), which
prepares for the unknown classes by allocating placeholders for both data and
classifier. In detail, learning data placeholders tries to anticipate open-set
class data, thus transforms closed-set training into open-set training.
Besides, to learn the invariant information between target and non-target
classes, we reserve classifier placeholders as the class-specific boundary
between known and unknown. The proposed Proser efficiently generates novel
class by manifold mixup, and adaptively sets the value of reserved open-set
classifier during training. Experiments on various datasets validate the
effectiveness of our proposed method.
