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• W3044803312 abstract "Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover invariances and regularities that help to generalize. In this paper we propose to apply a similar approach to the problem of object recognition across domains: our model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals on the same images. This secondary task helps the network to focus on object shapes, learning concepts like spatial orientation and part correlation, while acting as a regularizer for the classification task over multiple visual domains. Extensive experiments confirm our intuition and show that our multi-task method combining supervised and self-supervised knowledge shows competitive results with respect to more complex domain generalization and adaptation solutions. It also proves its potential in the novel and challenging predictive and partial domain adaptation scenarios." @default.
• W3044803312 created "2020-07-29" @default.
• W3044803312 creator A5004857169 @default.
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• W3044803312 creator A5073146276 @default.
• W3044803312 creator A5080303732 @default.
• W3044803312 date "2020-07-24" @default.
• W3044803312 modified "2023-09-24" @default.
• W3044803312 title "Self-Supervised Learning Across Domains" @default.
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• W3044803312 hasPublicationYear "2020" @default.
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