Active one-shot learning with prototypical networks

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Conference article in proceedings
Date
2019-01-01
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Language
en
Pages
6
583-588
Series
ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abstract
We consider the problem of active one-shot classification where a classifier needs to adapt to new tasks by requesting labels for one example per class from (potentially many) unlabeled examples. We propose a clustering approach to the problem. The features extracted with Prototypical Networks [1] are clustered using K-means and the label for one representative sample from each cluster is requested to label the whole cluster. We demonstrate good performance of this simple active adaptation strategy using image data.
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Boney , R & Ilin , A 2019 , Active one-shot learning with prototypical networks . in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning . European Symposium on Artificial Neural Networks (ESANN) , pp. 583-588 , European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , Bruges , Belgium , 24/04/2019 .