  Multi-class novelty detection is increasingly becoming an important area of
research due to the continuous increase in the number of object categories. It
tries to answer the pertinent question: given a test sample, should we even try
to classify it? We propose a novel solution using the concept of mixup
technique for novelty detection, termed as Segregation Network. During
training, a pair of examples are selected from the training data and an
interpolated data point using their convex combination is constructed. We
develop a suitable loss function to train our model to predict its constituent
classes. During testing, each input query is combined with the known class
prototypes to generate mixed samples which are then passed through the trained
network. Our model which is trained to reveal the constituent classes can then
be used to determine whether the sample is novel or not. The intuition is that
if a query comes from a known class and is mixed with the set of known class
prototypes, then the prediction of the trained model for the correct class
should be high. In contrast, for a query from a novel class, the predictions
for all the known classes should be low. The proposed model is trained using
only the available known class data and does not need access to any auxiliary
dataset or attributes. Extensive experiments on two benchmark datasets, namely
Caltech 256 and Stanford Dogs and comparisons with the state-of-the-art
algorithms justifies the usefulness of our approach.
