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Modern approaches for fast retrieval of similar vectors on billion-scaleddatasets rely on compressed-domain approaches such as binary sketches orproduct quantization. These methods minimize a certain loss, typically the meansquared error or other objective functions tailored to the retrieval problem.In this paper, we re-interpret popular methods such as binary hashing orproduct quantizers as auto-encoders, and point out that they implicitly makesuboptimal assumptions on the form of the decoder. We designbackward-compatible decoders that improve the reconstruction of the vectorsfrom the same codes, which translates to a better performance in nearestneighbor search. Our method significantly improves over binary hashing methodsor product quantization on popular benchmarks.

Nearest neighbor search with compact codes: A decoder perspective