| Resumo: | With the recent surge of big data, manually annotating datasets has become an impossible task. Asa result, domain adaptation has gained more importance than ever, since it allows the transfer oflearning from a labeled dataset to an unlabeled one. That is even truer for sensor networks, wherethe domain shifts between different sensors are usually substantial and models developed with aset of source sensors fail to generalize well to new target sensors.Adversarial neural networks, which were introduced in 2014 in the scope of generative adver-sarial neural networks, are a promising tool to address the domain adaptation problem. Despitebeing a recent idea, adversarial networks have already led to tremendous progress in multiple areasof the field. In this dissertation, we propose a new model for domain adaptation, specifically tunedfor sensor networks, that consists of two adversarial Long short-term memory (LSTM) networks.One that seeks to discriminate between source and target domains and another one that performsthe learning task. The fact that we take the temporal nature of the data into account by using anLSTM network for the discriminating task is a novel idea. We evaluate the performance of ourmodel by conducting extensive experiments and comparing it with other state of the art methods.Besides the proposed model, we include an exhaustive survey of domain adaptation methodsand an experimental analysis of their efficiency in different datasets. Additionally, we also discussseveral open lines of research in the field of domain adaptation that can serve as a guide for futurework in this area.
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