  If an unknown example that is not seen during training appears, most
recognition systems usually produce overgeneralized results and determine that
the example belongs to one of the known classes. To address this problem,
teacher-explorer-student (T/E/S) learning, which adopts the concept of open set
recognition (OSR) that aims to reject unknown samples while minimizing the loss
of classification performance on known samples, is proposed in this study. In
this novel learning method, overgeneralization of deep learning classifiers is
significantly reduced by exploring various possibilities of unknowns. Here, the
teacher network extracts some hints about unknowns by distilling the pretrained
knowledge about knowns and delivers this distilled knowledge to the student.
After learning the distilled knowledge, the student network shares the learned
information with the explorer network. Then, the explorer network shares its
exploration results by generating unknown-like samples and feeding the samples
to the student network. By repeating this alternating learning process, the
student network experiences a variety of synthetic unknowns, reducing
overgeneralization. Extensive experiments were conducted, and the experimental
results showed that each component proposed in this paper significantly
contributes to the improvement in OSR performance. As a result, the proposed
T/E/S learning method outperformed current state-of-the-art methods.
