The generic function of a feedforward neural net is to map patterns from one space to another. This mapping function, determined by the set of examples used to train the network, may be viewed as a hashing function – the Neuro-Hasher. This paper reports the experiments on using a backpropagation network with a dynamic hidden layer for finding an appropriate hashing function for a given populati...

The development of unsupervised hashing is advanced by the recent popular contrastive learning paradigm. However, previous learning-based works have been hampered (1) insufficient data similarity mining based on global-only image representations, and (2) hash code semantic loss caused augmentation. In this paper, we propose a novel method, namely Weighted Contrative Hashing (WCH), to take step ...

Hashing is a solved problem. It allows us to get constant time access for lookups. Hashing is also simple. It is safe to use an arbitrary method as a black box and expect good performance, and optimizations to hashing can only improve it by a negligible delta. Why are all of the previous statements plain wrong? That is what this paper is about. In this paper we thoroughly study hashing for inte...

Cascade Hashing is a new dynamic hashing scheme which is based on Spiral Storage. The purpose of this paper is first to give a unified exposition of Linear Hashing, Spiral Storage and other dynamic hashing schemes, and second to describe a new method for storing overflow records. The method stores the overflow records in the main file itself and clusters overflow records from each primary bucke...

DEFINITION Extendible Hashing is a dynamically updateable disk-based index structure which implements a hashing scheme utilizing a directory. The index is used to support exact match queries, i.e. find the record with a given key. Compared with the B+-tree index which also supports exact match queries (in logarithmic number of I/Os), Extendible Hashing has better expected query cost O(1) I/O. C...