Autoencoder based novelty detection for generalized zero shot learning

The problem of generalized zero-shot learning deals with the classification of test examples for which training data may or may not be available. Existing baseline algorithms connect the seen and unseen set of categories by learning functions to project the image data into the attribute space or vice versa. However, since the classification framework is trained only on the seen set of categories, the recognition performance is typically biased and algorithms have great difficulty in recognizing novel classes. In this work, we investigate the usefulness of a novelty detector to recognize a given data as coming from the seen or novel set. The proposed novelty detector is based on an autoencoder network structure with reconstruction and triplet cosine embedding losses which can be effectively trained using only the seen data and its categories. Experiments over a variety of benchmark datasets and zero-shot algorithms show the efficacy of the proposed approach.