The Power of Asymmetry in Binary Hashing
classification
💻 cs.LG
cs.CVcs.IR
keywords
binarydistancehammingsimilarityapproximatingdistincthashesaccurate
read the original abstract
When approximating binary similarity using the hamming distance between short binary hashes, we show that even if the similarity is symmetric, we can have shorter and more accurate hashes by using two distinct code maps. I.e. by approximating the similarity between $x$ and $x'$ as the hamming distance between $f(x)$ and $g(x')$, for two distinct binary codes $f,g$, rather than as the hamming distance between $f(x)$ and $f(x')$.
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