  Generative models are widely used for unsupervised learning with various
applications, including data compression and signal restoration. Training
methods for such systems focus on the generality of the network given limited
amount of training data. A less researched type of techniques concerns
generation of only a single type of input. This is useful for applications such
as constraint handling, noise reduction and anomaly detection. In this paper we
present a technique to limit the generative capability of the network using
negative learning. The proposed method searches the solution in the gradient
direction for the desired input and in the opposite direction for the undesired
input. One of the application can be anomaly detection where the undesired
inputs are the anomalous data. In the results section we demonstrate the
features of the algorithm using MNIST handwritten digit dataset and latter
apply the technique to a real-world obstacle detection problem. The results
clearly show that the proposed learning technique can significantly improve the
performance for anomaly detection.
