Efficient Multi-Domain Dictionary Learning with GANs

In this paper, we propose the multi-domain dictionary learn- ing (MDDL) to make dictionary learning-based classification more robust to data representing in different domains. We use adversarial neural networks to generate data in different styles, and collect all the generated data into a miscellaneous dictionary. To tackle the dictionary learning with many sam- ples, we compute the weighting matrix that compress the mis- cellaneous dictionary from multi-sample per class to single sample per class. We show that the time complexity solv- ing the proposed MDDL with weighting matrix is the same as solving the dictionary with single sample per class. More- over, since the weighting matrix could help the solver rely more on the training data, which possibly lie in the same do- main with the testing data, the classification could be more accurate.