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• W652269744 abstract "Zero-shot learning concerns learning how to recognise new classes from just a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. Here we describe a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state-of-the-art approaches on standard datasets. The approach is based on a more general framework which models the relationships between features, attributes, and classes as a network with two linear layers, where the weights of the top layer are not learned but are given by the environment. We further provide a learning bound on the generalisation error of this kind of approaches, by casting them as domain adaptation methods. In experiments carried out on three standard real datasets, we found that our approach is able to perform significantly better than the state of the art on all of them." @default.
• W652269744 created "2016-06-24" @default.
• W652269744 creator A5042899882 @default.
• W652269744 creator A5088601308 @default.
• W652269744 date "2017-01-01" @default.
• W652269744 modified "2023-10-11" @default.
• W652269744 title "An Embarrassingly Simple Approach to Zero-Shot Learning" @default.
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• W652269744 doi "https://doi.org/10.1007/978-3-319-50077-5_2" @default.
• W652269744 hasPublicationYear "2017" @default.
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