Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases exploiting single-source DA methods for learning target classifiers may lead to sub-optimal, if not poor, results. In addition, in many applications it is difficult to manually provide the domain labels for all source data points, i.e. latent domains should be automatically discovered. This paper introduces a novel Convolutional Neural Network (CNN) architecture which (i) automatically discovers latent domains in visual datasets and (ii) exploits this information to learn robust target classifiers. Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We test our approach on publicly-available datasets, showing that it outperforms state-of-the-art multi-source DA methods by a large margin.

Boosting Domain Adaptation by Discovering Latent Domains / Mancini, Massimiliano; Porzi, Lorenzo; Bulo, Samuel Rota; Caputo, Barbara; Ricci, Elisa. - (2018), pp. 3771-3780. (Intervento presentato al convegno CVPR tenutosi a Salt Lake City, UT, USA nel 18-23 June) [10.1109/CVPR.2018.00397].

Boosting Domain Adaptation by Discovering Latent Domains

Mancini, Massimiliano;Ricci, Elisa
2018-01-01

Abstract

Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases exploiting single-source DA methods for learning target classifiers may lead to sub-optimal, if not poor, results. In addition, in many applications it is difficult to manually provide the domain labels for all source data points, i.e. latent domains should be automatically discovered. This paper introduces a novel Convolutional Neural Network (CNN) architecture which (i) automatically discovers latent domains in visual datasets and (ii) exploits this information to learn robust target classifiers. Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We test our approach on publicly-available datasets, showing that it outperforms state-of-the-art multi-source DA methods by a large margin.
2018
IEEE/CVF Conference on Computer Vision and Pattern Recognition
Piscataway, NJ USA
IEEE
978-1-5386-6420-9
Mancini, Massimiliano; Porzi, Lorenzo; Bulo, Samuel Rota; Caputo, Barbara; Ricci, Elisa
Boosting Domain Adaptation by Discovering Latent Domains / Mancini, Massimiliano; Porzi, Lorenzo; Bulo, Samuel Rota; Caputo, Barbara; Ricci, Elisa. - (2018), pp. 3771-3780. (Intervento presentato al convegno CVPR tenutosi a Salt Lake City, UT, USA nel 18-23 June) [10.1109/CVPR.2018.00397].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/225604
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