Deep Hashing
51 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Deep Hashing models and implementationsMost implemented papers
Deep Hashing Network for Unsupervised Domain Adaptation
Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain.
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN
ssppp/GreedyHash
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NeurIPS 2018
To convert the input into binary code, hashing algorithm has been widely used for approximate nearest neighbor search on large-scale image sets due to its computation and storage efficiency.
Targeted Attack for Deep Hashing based Retrieval
jiawangbai/DHTA-master
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ECCV 2020
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
Deep Multi Query Image Retrieval
Existing methods are not based on hash codes.
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
kamwoh/orthohash
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NeurIPS 2021
In this work, we propose a novel deep hashing model with only a single learning objective.
Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks
SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets.
Feature Learning based Deep Supervised Hashing with Pairwise Labels
For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning.
Deep Supervised Hashing with Triplet Labels
The current state-of-the-art deep hashing method DPSH~\cite{li2015feature}, which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities.
Deep Discrete Hashing with Self-supervised Pairwise Labels
htconquer/ddh
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2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way?
Binary Generative Adversarial Networks for Image Retrieval
htconquer/BGAN
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By restricting the input noise variable of generative adversarial networks (GAN) to be binary and conditioned on the features of each input image, BGAN can simultaneously learn a binary representation per image, and generate an image plausibly similar to the original one.
