  Convolutional Neural Networks (CNNs) are commonly designed for closed set
arrangements, where test instances only belong to some "Known Known" (KK)
classes used in training. As such, they predict a class label for a test sample
based on the distribution of the KK classes. However, when used under the Open
Set Recognition (OSR) setup (where an input may belong to an "Unknown Unknown"
or UU class), such a network will always classify a test instance as one of the
KK classes even if it is from a UU class. As a solution, recently, data
augmentation based on Generative Adversarial Networks(GAN) has been used. In
this work, we propose a novel approach for mining a "Known UnknownTrainer" or
KUT set and design a deep OSR Network (OSRNet) to harness this dataset. The
goal isto teach OSRNet the essence of the UUs through KUT set, which is
effectively a collection of mined "hard Known Unknown negatives". Once trained,
OSRNet can detect the UUs while maintaining high classification accuracy on
KKs. We evaluate OSRNet on six benchmark datasets and demonstrate it
outperforms contemporary OSR methods.
