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Since Convolutional Neural Networks (CNNs) have become the leading learningparadigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-basedclassifiers have lost momentum in the community. This is because (1) suchalgorithms cannot use CNN activations as input features; (2) they cannot beused as final layer of CNN architectures for end-to-end training , and (3) theyare generally not scalable and hence cannot handle big data. This paperproposes a framework that addresses all these issues, thus bringing back NBNNson the map. We solve the first by extracting CNN activations from local patchesat multiple scale levels, similarly to [1]. We address simultaneously thesecond and third by proposing a scalable version of Naive Bayes Non-linearLearning (NBNL, [2]). Results obtained using pre-trained CNNs on standard sceneand domain adaptation databases show the strength of our approach, opening anew season for NBNNs.

When Na\"ive Bayes Nearest Neighbours Meet Convolutional Neural Networks