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arXiv · 1511.03853

When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks

Ilja Kuzborskij·Fabio Maria Carlucci·Barbara Caputo
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Abstract

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentum in the community. This is because (1) such algorithms cannot use CNN activations as input features; (2) they cannot be used as final layer of CNN architectures for end-to-end training , and (3) they are generally not scalable and hence cannot handle big data. This paper proposes a framework that addresses all these issues, thus bringing back NBNNs on the map. We solve the first by extracting CNN activations from local patches at multiple scale levels, similarly to [1]. We address simultaneously the second and third by proposing a scalable version of Naive Bayes Non-linear Learning (NBNL, [2]). Results obtained using pre-trained CNNs on standard scene and domain adaptation databases show the strength of our approach, opening a new season for NBNNs.

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Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo. 2015-11-17. When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks. https://arxiv.org/abs/1511.03853

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