  When performing data classification over a stream of continuously occurring
instances, a key challenge is to develop an open-world classifier that
anticipates instances from an unknown class. Studies addressing this problem,
typically called novel class detection, have considered classification methods
that reactively adapt to such changes along the stream. Importantly, they rely
on the property of cohesion and separation among instances in feature space.
Instances belonging to the same class are assumed to be closer to each other
(cohesion) than those belonging to different classes (separation).
Unfortunately, this assumption may not have large support when dealing with
high dimensional data such as images. In this paper, we address this key
challenge by proposing a semisupervised multi-task learning framework called
CSIM which aims to intrinsically search for a latent space suitable for
detecting labels of instances from both known and unknown classes.
Particularly, we utilize a convolution neural network layer that aids in the
learning of a latent feature space suitable for novel class detection. We
empirically measure the performance of CSIM over multiple realworld image
datasets and demonstrate its superiority by comparing its performance with
existing semi-supervised methods.
