Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
The brain structure in the collected data is complicated, thence, doctors are required to spend plentiful energy when diagnosing brain abnormalities.
To capture the underlying structure of live faces data in latent representation space, we propose to train the live face data only, with a convolutional Encoder-Decoder network acting as a Generator.
Anomaly detection is a fundamental problem in computer vision area with many real-world applications.
Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones.
One-class novelty detection is the process of determining if a query example differs from the training examples (the target class).