Deep hashing is a typical task that combines CO and ML, aiming to find an optimal code for each data from finite discrete possibilities via deep neural networks, so that similar data have shorter Hamming distance and dissimilar data have longer Hamming distance [5], [6].
Sep 17, 2020 · It explicitly estimates the uncertainty during training and leverages the uncertainty information to guide the approximation process.
Deep hashing is a typical task that combines CO and ML, aiming to find an optimal code for each data from finite discrete possibilities via deep neural networks ...
Feb 1, 2022 · It explicitly estimates the uncertainty during training and leverages the uncertainty information to guide the approximation process.
Deep Momentum Uncertainty Hashing (DMUH) [43] addresses the challenges in deep hashing by explicitly estimating and leveraging uncertainty during training.
Deep momentum uncertainty hashing. 作者：. Highlights：. • We are the first to explore the uncertainty of hashing bits during approximate optimization.
Discrete optimization is one of the most intractable problems in deep hashing. Previous methods usually mitigate this problem by binary approximation, ...
在本文中, 我们提议了一个新的深调不固定的混凝土( DMUH ) 。 它明确估计了培训期间的不确定性, 并利用不确定性信息引导近距离进程。 具体地说, 我们通过测量一个散乱网络 ...
Deep Momentum Uncertainty Hashing (DMUH) [43] addresses the challenges in deep hashing by explicitly estimating and leveraging uncertainty during training.
Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image re- trieval. How to optimize discrete hash bits is al- ways the ...