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Recent advance of large scale similarity search requires to learn deep representations that both strongly preserve similarities between data pairs and can be accurately quantized via vector quantization. Existing methods simply leverage quantization loss and similarity loss, which result in unexpectedly biased back-propagating gradients and affect the search performances. To this end, we propose a...
Recent advance of large scale similarity search involves using deeply learned representations to improve the search accuracy and use vector quantization methods to increase the search speed. However, how to learn deep representations that both strongly preserve similarities between data pairs and can be accurately quantized via vector quantization remains a challenging task. In this paper, we propose...
We present a novel nearest neighbor search scheme named aggregating tree (A-Tree) for high dimensional data that uses vector quantization encodings (VQ-encodings) to build a radix tree, and perform the nearest neighbor search by beam search. To search accurately and efficiently, we suggest VQ-encodings to satisfy locally aggregating encoding criterion: for any node of the corresponding A-Tree, neighboring...
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