Unsupervised hashing methods typically aim to preserve the similarity between data points
in a feature space by mapping them to binary hash codes. However, these methods often
overlook the fact that the similarity between data points in the continuous feature space may
not be preserved in the discrete hash code space, due to the limited similarity range of hash
codes. The similarity range is bounded by the code length and can lead to a problem known
as similarity collapse. That is, the positive and negative pairs of data points become less …

Unsupervised hashing methods typically aim to preserve the similarity between data points
in a feature space by mapping them to binary hash codes. However, these methods often
overlook the fact that the similarity between data points in the continuous feature space may
not be preserved in the discrete hash code space, due to the limited similarity range of hash
codes. The similarity range is bounded by the code length and can lead to a problem known
as similarity collapse. That is, the positive and negative pairs of data points become less …