Code for: Ferro, D., Gripon, V. and Jiang, X., 2016, July. Nearest neighbour search using binary neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 5106-5112). IEEE.
The code finds nearest neighbours in terms of Euclidean distance, and uses it for classification. The search is optimized by combining Product Quantization (PQ) and binary neural associative memories (Willshaw Neural Networks).
DOI http://dx.doi.org/10.1109/IJCNN.2016.7727873

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Nearest Neighbour Search using binary Neural Networks and Product Quantization

We face the problem of finding nearest neighbours in terms of Euclidean distance, Hamming distance or other distance metric. In order to accelerate the search for the nearest neighbour in large collection datasets, many methods rely on the coarse-fine approach. In this paper we propose to combine Product Quantization (PQ) and binary neural associative memories (Willshaw Neural Networks) to perform the coarse search.

README.md

Nearest Neighbour Search using binary Neural Networks and Product Quantization