  Open Set Recognition (OSR) has been an emerging topic. Besides recognizing
predefined classes, the system needs to reject the unknowns. Prototype learning
is a potential manner to handle the problem, as its ability to improve
intra-class compactness of representations is much needed in discrimination
between the known and the unknowns. In this work, we propose a novel Prototype
Mining And Learning (PMAL) framework. It has a prototype mining mechanism
before the phase of optimizing embedding space, explicitly considering two
crucial properties, namely high-quality and diversity of the prototype set.
Concretely, a set of high-quality candidates are firstly extracted from
training samples based on data uncertainty learning, avoiding the interference
from unexpected noise. Considering the multifarious appearance of objects even
in a single category, a diversity-based strategy for prototype set filtering is
proposed. Accordingly, the embedding space can be better optimized to
discriminate therein the predefined classes and between known and unknowns.
Extensive experiments verify the two good characteristics (i.e., high-quality
and diversity) embraced in prototype mining, and show the remarkable
performance of the proposed framework compared to state-of-the-arts.
