Deep hashing retrieval algorithm based on meta-learning

doi:10.11772/j.issn.1001-9081.2021040660

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2015-2021.DOI: 10.11772/j.issn.1001-9081.2021040660

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Deep hashing retrieval algorithm based on meta-learning

Yaru HAN¹, Lianshan YAN²(), Tao YAO¹

^1.School of Information and Electrical Engineering，Ludong University，Yantai Shandong 264025，China
^2.School of Information Science and Technology，Southwest Jiaotong University，Chengdu Sichuan 611756，China

Received:2021-04-25 Revised:2021-09-01 Accepted:2021-09-07 Online:2022-07-15 Published:2022-07-10
Contact: Lianshan YAN
About author:HAN Yaru， born in 1995， M. S. candidate. Her research interests include multimedia image retrieval， artificial intelligence， machine learning.
YAO Tao， born in 1981， Ph. D.， associate professor. His research interests include multimedia analysis and computing， computer vision， machine learning.
Supported by:
National Natural Science Foundation of China(61872170)

基于元学习的深度哈希检索算法

韩亚茹¹, 闫连山²(), 姚涛¹

^1.鲁东大学信息与电气工程学院，山东烟台 264025
^2.西南交通大学信息科学与技术学院，成都 611756

通讯作者: 闫连山
作者简介:韩亚茹（1995—），女，山东济南人，硕士研究生，主要研究方向：多媒体图像检索、人工智能、机器学习
姚涛（1981—），男，山东烟台人，副教授，博士，主要研究方向：多媒体分析与计算、计算机视觉、机器学习。
基金资助:
国家自然科学基金资助项目(61872170)

Abstract

Abstract:

With the development of mobile Internet technology， the scale of image data is getting larger and larger， and the large-scale image retrieval task has become an urgent problem. Due to the fast retrieval speed and very low storage consumption， the hashing algorithm has received extensive attention from researchers. Deep learning based hashing algorithms need a certain amount of high-quality training data to train the model to improve the retrieval performance. However， the existing hashing methods usually ignore the problem of imbalance of data categories in the dataset， which may reduce the retrieval performance. Aiming at this problem， a deep hashing retrieval algorithm based on meta-learning network was proposed， which can automatically learn the weighting function directly from the data. The weighting function is a Multi-Layer Perceptron （MLP） with only one hidden layer. Under the guidance of a small amount of unbiased meta data， the parameters of the weighting function were able to be optimized and updated simultaneously with the parameters during model training process. The updating equations of the meta-learning network parameters were able to be explained as： increasing the weights of samples which are consistent with the meta-learning data， and reducing the weights of samples which are not consistent with the meta-learning data. The impact of imbalanced data on image retrieval was able to be effectively reduced and the robustness of the model was able to be improved through the deep hashing retrieval algorithm based on meta-learning network. A large number of experiments were conducted on widely used benchmark datasets such as CIFAR-10. The results show that the mean Average Precision （mAP） of the hashing algorithm based on meta-learning network is the highest with large imbalanced rate；especially， under the condition of imbalanced ratio=200， the mAP of the proposed algorithm is 0.54 percentage points，30.93 percentage points and 48.43 percentage points higher than those of central similarity quantization algorithm， Asymmetric Deep Supervised Hashing （ADSH） algorithm and Fast Scalable Supervised Hashing （FSSH） algorithm.

Key words: deep learning, hashing algorithm, imbalanced data, meta-learning, image retrieval

摘要：

随着移动互联网技术的发展，图像数据的规模越来越大，大规模图像检索任务已经成为了一个紧要的问题。由于检索速度快和存储消耗低，哈希算法受到了研究者的广泛关注。基于深度学习的哈希算法要达到较好的检索性能，需要一定数量的高质量训练数据来训练模型。然而现存的哈希方法通常忽视了数据集存在数据类别非平衡的问题，而这可能会降低检索性能。针对上述问题，提出了一种基于元学习网络的深度哈希检索算法。所提算法可以直接从数据中自动学习加权函数。该加权函数是只有一个隐含层的多层感知机（MLP），在少量无偏差元数据的指导下，加权函数的参数可以和模型训练过程中的参数同时进行优化更新。元学习网络参数的更新方程可以解释为：较符合元学习数据的样本权重将被提高，而不符合元学习数据的样本权重将被减小。基于元学习网络的深度哈希检索算法可以有效减少非平衡数据对图像检索的影响，并可以提高模型的鲁棒性。在CIFAR-10等广泛使用的基准数据集上进行的大量实验表明，在非平衡比率较大时，所提算法的平均准确率均值（mAP）最佳；在非平均比率为200的条件下，所提算法的mAP比中心相似度量化算法、非对称深度监督哈希（ADSH）算法和快速可扩展监督哈希（FSSH）算法分别提高0.54个百分点，30.93个百分点和48.43个百分点。

关键词: 深度学习, 哈希算法, 非平衡数据, 元学习, 图像检索

CLC Number:

TP183

Yaru HAN, Lianshan YAN, Tao YAO. Deep hashing retrieval algorithm based on meta-learning[J]. Journal of Computer Applications, 2022, 42(7): 2015-2021.

韩亚茹, 闫连山, 姚涛. 基于元学习的深度哈希检索算法[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2015-2021.

Figures/Tables 5

References 49

1	张楚涵，张家侨，冯剑琳. AKNN-Qalsh： PostgreSQL系统高维空间近似最近邻检索插件［J］. 中山大学学报（自然科学版）， 2019， 58（3）： 79-85.
	ZHANG C H， ZHANG J Q， FENG J L. AKNN-Qalsh： an approximate KNN search extension for high-dimensional data in PostgreSQL［J］. Acta Scientiarum Naturalium Universitatis Sunyatseni， 2019， 58（3）：79-85.
2	陈诚，邹焕新，邵宁远，等. 面向遥感影像的深度语义哈希检索［J］. 中国图象图形学报， 2018， 24（4）： 655-663.
	CHEN C， ZOU H X， SHAO N Y， et al. Deep semantic Hashing retrieval of remote sensing images［J］. Journal of Image and Graphics， 2019， 24（4）： 655-663.
3	DATAR M， IMMORLICA N， INDYK P， et al. Locality-sensitive hashing scheme based on p-stable distributions［C］// Proceedings of the 20th Annual Symposium on Computational Geometry. New York： ACM， 2004： 253-262. 10.1145/997817.997857
4	康松林，刘楚楚，樊晓平，等. WOS-ELM算法在入侵检测中的研究［J］. 小型微型计算机系统， 2015， 36（8）： 1779-1783. 10.3969/j.issn.1000-1220.2015.08.022
	KANG S L， LIU C C， FAN X P， et al. Research on intrusion detection based on WOS-ELM algorithm［J］. Journal of Chinese Computer Systems， 2015， 36（8）： 1779-1783. 10.3969/j.issn.1000-1220.2015.08.022
5	ZIĘBA M， TOMCZAK S K， TOMCZAK J M. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction［J］. Expert Systems with Applications， 2016， 58： 93-101. 10.1016/j.eswa.2016.04.001
6	KHATAMI A， BABAIE M， KHOSRAVI A， et al. Parallel deep solutions for image retrieval from imbalanced medical imaging archives［J］. Applied Soft Computing， 2018， 63： 197-205. 10.1016/j.asoc.2017.11.024
7	LAKE B M， SALAKHUTDINOV R， TENENBAUM J B. Human-level concept learning through probabilistic program induction［J］. Science， 2015， 350（6266）： 1332-1338. 10.1126/science.aab3050
8	FINN C， ABBEEL P， LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks［C］// Proceedings of the 34th International Conference on Machine Learning. New York： JMLR.org， 2017： 1126-1135.
9	JIANG L， ZHOU Z Y， LEUNG T， et al. MentorNet： learning data-driven curriculum for very deep neural networks on corrupted labels［C］// Proceedings of the 35th International Conference on Machine Learning. New York： JMLR.org， 2018： 2304-2313.
10	WU L J， TIAN F， XIA Y C， et al. Learning to teach with dynamic loss functions［C］// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2018： 6467-6478.
11	WEISS Y， TORRALBA A， FERGUS R. Spectral hashing［C］// Proceedings of the 21st International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2008： 1753-1760.
12	GONG Y C， LAZEBNIK S， GORDO A， et al. Iterative quantization a procrustean approach to learning binary codes for large-scale image retrieval［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2013， 35（12）： 2916-2929. 10.1109/tpami.2012.193
13	SHEN F M， XU Y， LIU L， et al. Unsupervised deep hashing with similarity-adaptive and discrete optimization［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2018， 40（12）： 3034-3044. 10.1109/tpami.2018.2789887
14	KULIS B， DARRELL T. Learning to hash with binary reconstructive embeddings［C］// Proceedings of the 22nd International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2009： 1042-1050.
15	LIU W， WANG J， JI R R， et al. Supervised hashing with kernels［C］// Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2012： 2074-2081. 10.1109/cvpr.2012.6247912
16	SHEN F M， SHEN C H， LIU W， et al. Supervised discrete hashing［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2015： 37-45. 10.1109/cvpr.2015.7298598
17	YAO T， LONG F C， MEI T， et al. Deep semantic-preserving and ranking-based hashing for image retrieval［C］// Proceedings of the 25th International Joint Conference on Artificial Intelligence. California： ijcai.org， 2016： 3931-3937.
18	LIU H M， WANG R P， SHAN S G， et al. Deep supervised hashing for fast image retrieval［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 2064-2072. 10.1109/cvpr.2016.227
19	LI Q， SUN Z N， HE R， et al. Deep supervised discrete hashing［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2017： 2479-2488.
20	DONG Q， GONG S G， ZHU X T. Class rectification hard mining for imbalanced deep learning［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 1869-1878. 10.1109/iccv.2017.205
21	LIU X Y， WU J X， ZHOU Z H. Exploratory undersampling for class-imbalance learning［J］. IEEE Transactions on Systems， Man， and Cybernetics， Part B （Cybernetics）， 2009， 39（2）： 539-550. 10.1109/tsmcb.2008.2007853
22	MALDONADO S， MONTECINOS C. Robust classification of imbalanced data using one-class and two-class SVM-based multiclassifiers［J］. Intelligent Data Analysis， 2014， 18（1）： 95-112. 10.3233/ida-130630
23	ZHANG Z L， LUO X G， GARCÍA S， et al. Cost-sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers［J］. Applied Soft Computing， 2017， 56： 357-367. 10.1016/j.asoc.2017.03.016
24	SUN Y， LI Z L， LI X W， et al. Classifier selection and ensemble model for multi-class imbalance learning in education grants prediction［J］. Applied Artificial Intelligence， 2021， 35（4）： 290-303. 10.1080/08839514.2021.1877481
25	KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks［C］// Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2012： 1097-1105.
26	GIRSHICK R， DONAHUE J， DARRELL T， et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2014： 580-587. 10.1109/cvpr.2014.81
27	ZHEN Y， YEUNG D Y. A probabilistic model for multimodal hash function learning［C］// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2012： 940-948. 10.1145/2339530.2339678
28	LUO X， NIE L Q， HE X G， et al. Fast scalable supervised hashing［C］// Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2018： 735-744. 10.1145/3209978.3210035
29	JIANG Q Y， LI W J. Asymmetric deep supervised hashing［C］// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto， CA： AAAI Press， 2018： 3342-3349. 10.1609/aaai.v32i1.11814
30	YUAN L， WANG T， ZHANG X P， et al. Central similarity quantization for efficient image and video retrieval ［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 3080-3089. 10.1109/cvpr42600.2020.00315
31	WANG J， KUMAR S， CHANG S F. Semi-supervised hashing for large-scale search［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2012， 34（12）： 2393-2406. 10.1109/tpami.2012.48
32	GUI J， LIU T L， SUN Z N， et al. Fast supervised discrete hashing［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2018， 40（2）： 490-496. 10.1109/tpami.2017.2678475
33	HUANG C， LI Y N， LOY C C， et al. Learning deep representation for imbalanced classification［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 5375-5384. 10.1109/cvpr.2016.580
34	ZHAO F， HUANG Y Z， WANG L， et al. Deep semantic ranking based hashing for multi-label image retrieval［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2015： 1556-1564. 10.1109/cvpr.2015.7298763
35	YANG H F， LIN K， CHEN C S. Supervised learning of semantics-preserving hash via deep convolutional neural networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2018， 40（2）： 437-451. 10.1109/tpami.2017.2666812
36	LI X， LIN G S， SHEN C H， et al. Learning hash functions using column generation［C］// Proceedings of the 30th International Conference on Machine Learning. New York： JMLR.org， 2013： 142-150.
37	ZEILER M D， FERGUS R. Visualizing and understanding convolutional networks［C］// Proceedings of the 2014 European Conference on Computer Vision， LNCS 8689. Cham： Springer， 2014： 818-833.
38	OQUAB M， BOTTOU L， LAPTEV I， et al. Learning and transferring mid-level image representations using convolutional neural networks［C］// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2014： 1717-1724. 10.1109/cvpr.2014.222
39	WANG J D， ZHANG T， SONG J K， et al. A survey on learning to hash［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2018， 40（4）： 769-790. 10.1109/tpami.2017.2699960
40	LAI H J， PAN Y， LIU Y， et al. Simultaneous feature learning and hash coding with deep neural networks［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2015： 3270-3278. 10.1109/cvpr.2015.7298947
41	SHU J， XIE Q， YI L X， et al. Meta-weight-net： learning an explicit mapping for sample weighting［C/OL］// Proceedings of the 2019 Conference and Workshop on Neural Information Processing Systems. ［2021-02-21］..
42	LIN K， YANG H F， HSIAO J H， et al. Deep learning of binary hash codes for fast image retrieval［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2015： 27-35. 10.1109/cvprw.2015.7301269
43	LU X Q， ZHENG X T， LI X L. Latent semantic minimal hashing for image retrieval［J］. IEEE Transactions on Image Processing， 2017， 26（1）： 355-368. 10.1109/tip.2016.2627801
44	LIN K， LU J W， CHEN C S， et al. Learning compact binary descriptors with unsupervised deep neural networks［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 1183-1192. 10.1109/cvpr.2016.133
45	NI B B， YAN S C， KASSIM A. Learning a propagable graph for semisupervised learning： classification and regression［J］. IEEE Transactions on Knowledge and Data Engineering， 2012， 24（1）： 114-126. 10.1109/tkde.2010.209
46	柯圣财，赵永威，李弼程，等. 基于卷积神经网络和监督核哈希的图像检索方法［J］. 电子学报， 2017， 45（1）：157-163. 10.3969/j.issn.0372-2112.2017.01.022
	KE S C， ZHAO Y W， LI B C， et al. Image retrieval based on convolutional neural network and kernel-based supervised Hashing［J］. Acta Electronica Sinica， 2017， 45（1）： 157-163. 10.3969/j.issn.0372-2112.2017.01.022
47	王珊，王会举，覃雄派，等. 架构大数据：挑战、现状与展望［J］. 计算机学报， 2011， 34（10）： 1741-1752. 10.3724/sp.j.1016.2011.01741
	WANG S， WANG H J， QIN X P， et al. Architecting big data： challenges， studies and forecasts［J］. Chinese Journal of Computers， 2011， 34（10）： 1741-1752. 10.3724/sp.j.1016.2011.01741
48	艾列富，于俊清，管涛，等. 大规模图像特征检索中查询结果的自适应过滤［J］. 计算机学报， 2015， 38（1）： 122-132.
	AI L F， YU J Q， GUAN T， et al. Adaptively filtering query results for large scale image feature retrieval［J］. Chinese Journal of Computers， 2015， 38（1）： 122-132.
49	CHAWLA N V， BOWYER K W， HALL L O， et al. SMOTE： synthetic minority over-sampling technique［J］. Journal of Artificial Intelligence Research， 2002， 16： 321-357. 10.1613/jair.953

算法	CIFAR-10				CIFAR-100				STL-10
算法	16 bit	32 bit	48 bit	64 bit	16 bit	32 bit	48 bit	64 bit	16 bit	32 bit	48 bit	64 bit
ADSH^［29］	91.82	93.12	93.91	94.07	3.18	20.85	58.97	70.64	91.76	93.29	93.56	93.07
FSSH^［28］	62.17	67.46	68.12	68.84	14.81	19.23	22.91	25.34	62.43	67.55	69.03	69.23
CSQ^［30］	93.23	95.18	96.21	96.23	30.29	33.31	48.56	58.84	67.55	69.23	79.25	82.56
本文算法	93.51	93.45	93.72	93.92	70.51	74.32	78.42	79.13	92.25	92.85	93.13	94.65

算法	CIFAR-10				CIFAR-100				STL-10
算法	16 bit	32 bit	48 bit	64 bit	16 bit	32 bit	48 bit	64 bit	16 bit	32 bit	48 bit	64 bit
ADSH^［29］	91.82	93.12	93.91	94.07	3.18	20.85	58.97	70.64	91.76	93.29	93.56	93.07
FSSH^［28］	62.17	67.46	68.12	68.84	14.81	19.23	22.91	25.34	62.43	67.55	69.03	69.23
CSQ^［30］	93.23	95.18	96.21	96.23	30.29	33.31	48.56	58.84	67.55	69.23	79.25	82.56
本文算法	93.51	93.45	93.72	93.92	70.51	74.32	78.42	79.13	92.25	92.85	93.13	94.65

算法	CIFAR-10					CIFAE-100					STL-10
算法	200	100	50	20	10	200	100	50	20	10	200	100	50	20	10
ADSH^［29］	39.74	38.87	51.91	74.72	92.91	23.44	24.12	24.94	27.58	30.11	56.16	60.99	80.05	92.24	94.68
FSSH^［28］	22.24	45.37	46.52	53.55	58.76	12.92	13.79	14.47	15.85	16.72	41.76	45.06	48.54	53.42	59.93
CSQ^［30］	70.13	76.98	77.25	86.68	89.14	13.52	15.66	26.22	27.83	28.19	57.23	62.14	73.16	79.86	85.17
本文算法	70.67	77.91	78.93	85.28	87.88	22.23	30.14	38.59	40.10	51.31	58.23	63.38	73.56	79.97	84.08

算法	CIFAR-10					CIFAE-100					STL-10
算法	200	100	50	20	10	200	100	50	20	10	200	100	50	20	10
ADSH^［29］	39.74	38.87	51.91	74.72	92.91	23.44	24.12	24.94	27.58	30.11	56.16	60.99	80.05	92.24	94.68
FSSH^［28］	22.24	45.37	46.52	53.55	58.76	12.92	13.79	14.47	15.85	16.72	41.76	45.06	48.54	53.42	59.93
CSQ^［30］	70.13	76.98	77.25	86.68	89.14	13.52	15.66	26.22	27.83	28.19	57.23	62.14	73.16	79.86	85.17
本文算法	70.67	77.91	78.93	85.28	87.88	22.23	30.14	38.59	40.10	51.31	58.23	63.38	73.56	79.97	84.08

[1]	Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
[2]	Xiyuan WANG, Zhancheng ZHANG, Shaokang XU, Baocheng ZHANG, Xiaoqing LUO, Fuyuan HU. Unsupervised cross-domain transfer network for 3D/2D registration in surgical navigation [J]. Journal of Computer Applications, 2024, 44(9): 2911-2918.
[3]	Yunchuan HUANG, Yongquan JIANG, Juntao HUANG, Yan YANG. Molecular toxicity prediction based on meta graph isomorphism network [J]. Journal of Computer Applications, 2024, 44(9): 2964-2969.
[4]	Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
[5]	Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877.
[6]	Qiangkui LENG, Xuezi SUN, Xiangfu MENG. Oversampling method for imbalanced data based on sample potential and noise evolution [J]. Journal of Computer Applications, 2024, 44(8): 2466-2475.
[7]	Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557.
[8]	Yanjie GU, Yingjun ZHANG, Xiaoqian LIU, Wei ZHOU, Wei SUN. Traffic flow forecasting via spatial-temporal multi-graph fusion [J]. Journal of Computer Applications, 2024, 44(8): 2618-2625.
[9]	Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction [J]. Journal of Computer Applications, 2024, 44(8): 2643-2650.
[10]	Zheng WU, Zhiyou CHENG, Zhentian WANG, Chuanjian WANG, Sheng WANG, Hui XU. Deep learning-based classification of head movement amplitude during patient anaesthesia resuscitation [J]. Journal of Computer Applications, 2024, 44(7): 2258-2263.
[11]	Huanhuan LI, Tianqiang HUANG, Xuemei DING, Haifeng LUO, Liqing HUANG. Public traffic demand prediction based on multi-scale spatial-temporal graph convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2065-2072.
[12]	Zhi ZHANG, Xin LI, Naifu YE, Kaixi HU. DKP： defending against model stealing attacks based on dark knowledge protection [J]. Journal of Computer Applications, 2024, 44(7): 2080-2086.
[13]	Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks [J]. Journal of Computer Applications, 2024, 44(7): 2310-2318.
[14]	Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199.
[15]	Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215.

Deep hashing retrieval algorithm based on meta-learning

基于元学习的深度哈希检索算法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 49

Related Articles 15

Recommended Articles

Metrics