  We present a conceptually new and flexible method for multi-class open set
classification. Unlike previous methods where unknown classes are inferred with
respect to the feature or decision distance to the known classes, our approach
is able to provide explicit modelling and decision score for unknown classes.
The proposed method, called Gener- ative OpenMax (G-OpenMax), extends OpenMax
by employing generative adversarial networks (GANs) for novel category image
synthesis. We validate the proposed method on two datasets of handwritten
digits and characters, resulting in superior results over previous deep
learning based method OpenMax Moreover, G-OpenMax provides a way to visualize
samples representing the unknown classes from open space. Our simple and
effective approach could serve as a new direction to tackle the challenging
multi-class open set classification problem.

Computer Vision and Pattern Recognition