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Domain Generalization with Domain-Specific Aggregation Modules

  • Antonio D’InnocenteEmail author
  • Barbara Caputo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

Abstract

Visual recognition systems are meant to work in the real world. For this to happen, they must work robustly in any visual domain, and not only on the data used during training. Within this context, a very realistic scenario deals with domain generalization, i.e. the ability to build visual recognition algorithms able to work robustly in several visual domains, without having access to any information about target data statistic. This paper contributes to this research thread, proposing a deep architecture that maintains separated the information about the available source domains data while at the same time leveraging over generic perceptual information. We achieve this by introducing domain-specific aggregation modules that through an aggregation layer strategy are able to merge generic and specific information in an effective manner. Experiments on two different benchmark databases show the power of our approach, reaching the new state of the art in domain generalization.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sapienza University of RomeRomeItaly
  2. 2.Italian Institute of TechnologyMilanItaly

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