Simultaneous Classification and Novelty Detection Using Deep Neural Networks
Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect novel class distributions and therefore, most of the classification algorithms proposed make the assumption that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect novel class distributions without compromising much of its classification accuracy on the test examples of known classes. Experimental results on the CIFAR 100 and MiniImagenet data sets demonstrate the effectiveness of the proposed algorithm. The way this method was constructed also makes it suitable for training any classification algorithm that is based on Maximum Likelihood methods.