Efficient MultiDomain Dictionary Learning With GANS In this paper, we propose the multidomain dictionary learning (MDDL) to make dictionary learningbased 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 samples, we compute the weighting matrix that compress the miscellaneous dictionary from multisample per class to single sample per class. We show that the time complexity solving the proposed MDDL with weighting matrix is the same as solving the dictionary with single sample per class. Moreover, since the weighting matrix could help the solver rely more on the training data, which possibly lie in the same domain with the testing data, the classification could be more accurate.

LowResource Text Classification Using DomainAdversarial Learning Deep learning techniques have recently shown to be successful in many natural language processing tasks forming stateoftheart systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domainadversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural network in lowresource and zeroresource settings in new target domains or languages. In case of new languages, we show that monolingual wordvectors can be directly used for training without prealignment. Their projection into a common space can be learnt adhoc at training time reaching the final performance of pretrained multilingual wordvectors.