Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
We assume that training data is available to describe only the inlier distribution.
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
Event handlers have wide range of applications such as medical assistant systems and fire suppression systems.