In recent years, great advances in Domain Adaptation (DA) have been possible through deep neural networks. While this is true even for multi-source scenarios, most of the methods are based on the assumption that the domain to which each sample belongs is known a priori. However, in practice, we might have a source domain composed by a mixture of multiple sub-domains, without any prior about the sub-domain to which each source sample belongs. In this case, while multi-source DA methods are not applicable, restoring to single-source ones may lead to sub-optimal results. In this work, we explore a recent direction in deep domain adaptation: automatically discovering latent domains in visual datasets. Previous works address this problem by using a domain prediction branch, trained with an entropy loss. Here we present a novel formulation for training the domain prediction branch which exploits (i) domain prediction output for various perturbations of the input features and (ii) the min-entropy consensus loss, which forces the predictions of the perturbation to be both consistent and with low entropy. We compare our approach to the previous state-of-the-art on publicly-available datasets, showing the effectiveness of our method both quantitatively and qualitatively.

Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency / Mancini, Massimiliano; Porzi, Lorenzo; Cermelli, Fabio; Caputo, Barbara. - (2019), pp. 390-401. ((Intervento presentato al convegno International Conference on Image Analysis and Processing [10.1007/978-3-030-30645-8_36].

Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency

CERMELLI, FABIO;Caputo Barbara
2019

Abstract

In recent years, great advances in Domain Adaptation (DA) have been possible through deep neural networks. While this is true even for multi-source scenarios, most of the methods are based on the assumption that the domain to which each sample belongs is known a priori. However, in practice, we might have a source domain composed by a mixture of multiple sub-domains, without any prior about the sub-domain to which each source sample belongs. In this case, while multi-source DA methods are not applicable, restoring to single-source ones may lead to sub-optimal results. In this work, we explore a recent direction in deep domain adaptation: automatically discovering latent domains in visual datasets. Previous works address this problem by using a domain prediction branch, trained with an entropy loss. Here we present a novel formulation for training the domain prediction branch which exploits (i) domain prediction output for various perturbations of the input features and (ii) the min-entropy consensus loss, which forces the predictions of the perturbation to be both consistent and with low entropy. We compare our approach to the previous state-of-the-art on publicly-available datasets, showing the effectiveness of our method both quantitatively and qualitatively.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2785960