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Deep Semantic Hashing with Generative Adversarial Networks
[article]
2018
arXiv
pre-print
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Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an ...
This paper studies the exploration of generating synthetic data through semi-supervised generative adversarial networks (GANs), which leverages largely unlabeled and limited labeled training data to produce ...
(8) Deep Semantic Hashing with Generative Adversarial Networks (DSH-GANs) is our proposal in this paper. ...
arXiv:1804.08275v1
fatcat:y5jiqkg2mbhd3nzeuxvfzc4lsq
Deep Semantic Hashing with Generative Adversarial Networks
2017
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
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Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an ...
This paper studies the exploration of generating synthetic data through semisupervised generative adversarial networks (GANs), which leverages largely unlabeled and limited labeled training data to produce ...
(8) Deep Semantic Hashing with Generative Adversarial Networks (DSH-GANs) is our proposal in this paper. ...
doi:10.1145/3077136.3080842
dblp:conf/sigir/QiuPYM17
fatcat:h5jc4hkgeng5rnhbzu6gou4f3e
Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
[article]
2021
arXiv
pre-print
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To the best of our knowledge, this is the first generation-based method to attack deep hashing networks. ...
However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field. ...
network to generate semantically preserved hash codes. ...
arXiv:2105.07553v1
fatcat:2y6tg2vsrnd35afb7ylbsl23hi
Semi-supervised Generative Adversarial Hashing for Image Retrieval
[chapter]
2018
Lecture Notes in Computer Science
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In this paper, inspired by the idea of generative models and the minimax two-player game, we propose a novel semi-supervised generative adversarial hashing (SSGAH) approach. ...
However, those deep hashing models are usually trained with supervised information, which is rare and expensive in practice, especially class labels. ...
Deep Semantic Hashing with GANs (DSH-GANs) [21] minimizes the empirical error over synthetic data generated conditioned on class labels based on deep architecture. ...
doi:10.1007/978-3-030-01267-0_29
fatcat:tczbtvxjsjcixheybhrdgclyve
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
[article]
2020
arXiv
pre-print
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Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. ...
Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. ...
[151] developed a novel deep hashing approach, termed as Multi-Task Consistency Preserving Adversarial Hashing (CPAH), to capture cross-modal semantic correlation. ...
arXiv:2006.08159v1
fatcat:g4467zmutndglmy35n3eyfwxku
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
[article]
2018
arXiv
pre-print
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In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion ...
The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. ...
In the first phase, modality-specific features from separate generator networks are associated with each other in a common semantic space. ...
arXiv:1804.01223v1
fatcat:k76qsfqyzba7deegi5ag7l5fcu
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion ...
The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. ...
This may be because two adversarial networks are used in our framework, with which SSAH can more
Conclusion In this work, we proposed a novel deep hashing approach, dubbed self-supervised adversarial ...
doi:10.1109/cvpr.2018.00446
dblp:conf/cvpr/LiDL0GT18
fatcat:btx7fqba2raejio3txomwed3zi
Progressive Generative Hashing for Image Retrieval
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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generative adversarial networks (GANs). ...
However, without semantic label information, the unsupervised deep hashing still remains an open question. ...
To exhaustively exploit the power of GAN itself, this paper proposes a novel progressive generative adversarial frame- work to help learn a discriminative deep hashing network in an unsupervised way. ...
doi:10.24963/ijcai.2018/121
dblp:conf/ijcai/MaHDHLL18
fatcat:fgl24i644bdyfh37wl37dhsfxq
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
2019
IEEE Transactions on Image Processing
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2018 pre-print arXiv:1804.11013v1 fatcat:rt4vds4rkzb33luplhsnkby5dmOther Versions
To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further proposed upon the adversarial training to strengthen the correlations between inputs and corresponding ...
In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. ...
Generative Adversarial Networks Recently, generative adversarial networks (GANs) have been proposed to estimate a generative model by an adversarial training process [26] , and GANs-based networks can ...
doi:10.1109/tip.2018.2878970
fatcat:oy33fwr4mje5fd2njqzmioeq2a
SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation
[article]
2019
arXiv
pre-print
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The SSAH method consists of an adversarial network (A-Net) and a hashing network (H-Net). ...
The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semi-supervised learning. ...
Compared with traditional ones, deep hashing methods usually Figure 1 : To obtain the optimal boundary for points with similar hashing codes, we propose a novel self-paced deep adversarial hashing to ...
arXiv:1911.08688v1
fatcat:4gachgfntbaq5kzplq2e57yxce
SSAH: Semi-Supervised Adversarial Deep Hashing with Self-Paced Hard Sample Generation
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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The SSAH method consists of an adversarial network (A-Net) and a hashing network (H-Net). ...
The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semi-supervised learning. ...
Compared with traditional ones, deep hashing methods usually achieve better re- Figure 1 : To obtain the optimal boundary for points with similar hashing codes, we propose a novel self-paced deep adversarial ...
doi:10.1609/aaai.v34i07.6773
fatcat:gpnysdgguffezmo2wsagypl2ya
A Decade Survey of Content Based Image Retrieval using Deep Learning
[article]
2020
arXiv
pre-print
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Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. ...
The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. ...
A semi-supervised self-pace adversarial hashing (SSAH) method is discovered in [117] by integrating an adversarial network (ANet) with a hashing network (H-Net). ...
arXiv:2012.00641v1
fatcat:2zcho2szpzcc3cs6uou3jpcley
TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning ...
Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. ...
Generator network and discriminative sub-network constitute a normal conditional GANs. Semantic hashing sub-network is a normal image hashing network based on CNN. ...
doi:10.24963/ijcai.2017/491
dblp:conf/ijcai/ZhaoDGHG17
fatcat:ajwn5xn4ejhqrpk7q3633eo5dm
SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network
[article]
2018
arXiv
pre-print
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The main contributions can be summarized as follows: (1) We propose a novel generative adversarial network for cross-modal hashing. ...
To address these problems, in this paper we propose a novel Semi-supervised Cross-Modal Hashing approach by Generative Adversarial Network (SCH-GAN). ...
Generative Adversarial Network Generative Adversarial Network (GAN) [29] is first proposed to estimate generative model by an adversarial process. ...
arXiv:1802.02488v1
fatcat:3zxx64d7eza6xk6gt4rkmyb4vq
Deep Multi-level Semantic Hashing for Cross-modal Retrieval
2019
IEEE Access
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In this paper, the multi-level semantic supervision generating approach is proposed by exploring the label relevance. ...
INDEX TERMS Cross-modal retrieval, deep learning, hashing method, multi-label learning. ...
They proposed to use semi-supervised hashing approach by generative adversarial network. ...
doi:10.1109/access.2019.2899536
fatcat:xynopqlgyfhe3ef6su55zqczim
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