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We present a Gaussian kernel loss function and training algorithm forconvolutional neural networks that can be directly applied to both distancemetric learning and image classification problems. Our method treats alltraining features from a deep neural network as Gaussian kernel centres andcomputes loss by summing the influence of a feature's nearby centres in thefeature embedding space. Our approach is made scalable by treating it as anapproximate nearest neighbour search problem. We show how to make end-to-endlearning feasible, resulting in a well formed embedding space, in whichsemantically related instances are likely to be located near one another,regardless of whether or not the network was trained on those classes. Ourapproach outperforms state-of-the-art deep metric learning approaches onembedding learning challenges, as well as conventional softmax classificationon several datasets.

Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels