2013
NIPS
NeurIPS 2013
The Power of Asymmetry in Binary Hashing
Abstract
When approximating binary similarity using the hamming distance between short binary hashes, we shown 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 $f(x')$.
🧭
Keyword Pioneer
— asymmetric hashing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Machine Learning, Mathematics & Optimization, Natural Language Processing
🌉
Interdisciplinary Bridge
— Computer Science and Machine Learning
📈
Trend Setter
— Metric Learning
Authors
Topics
Machine Learning > Core Methods > Metric Learning
Machine Learning > Core Methods > Embedding Learning
Computer Science > Foundations > Algorithms
Computer Science > Applications > Information Retrieval
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Learning Types > Metric Learning
Machine Learning > Core Methods > Retrieval