Computer Science ›› 2023, Vol. 50 ›› Issue (6): 225-235.doi: 10.11896/jsjkx.220900197
• Computer Graphics & Multimedia • Previous Articles Next Articles
CAI Shaotian, CHEN Xiaojun, CHEN Longteng, QIU Liping
CLC Number:
[1]XIE J,GIRSHICK R,FARHADI A.Unsupervised deep embedding for clustering analysis[C]//International Conference on Machine Learning.PMLR,2016:478-487. [2]YANG B,FU X,SIDIROPOULOS N D,et al.Towards k-means-friendly spaces:Simultaneous deep learning and clustering[C]//International Conference on Machine Learning.PMLR,2017:3861-3870. [3]TIAN K,ZHOU S,GUAN J.DEEPCLUSTER:A general clustering framework based on deep learning[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Cham:Springer,2017:809-825. [4]AGGARWAL C C,WOLF J L,YU P S,et al.Fast algorithms for projected clustering[J].ACM SIGMoD Record,1999,28(2):61-72. [5]SHAHAM U,STANTON K,LI H,et al.Spectralnet:Spectralclustering using deep neural networks[J].arXiv:1801.01587,2018. [6]YANG J,PARIKH D,BATRA D.Joint unsupervised learning of deep representations and image clusters[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:5147-5156. [7]CHEN T,KORNBLITH S,NOROUZI M,et al.A simpleframework for contrastive learning of visual representations[C]//International Conference on Machine Learning.PMLR,2020:1597-1607. [8]HE K,FAN H,WU Y,et al.Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:9729-9738. [9]JI X,HENRIQUES J F,VEDALDI A.Invariant informationclustering for unsupervised image classification and segmentation[C]//Proceedings of the IEEE/CVF International Confe-rence on Computer Vision.2019:9865-9874. [10]LI Y,HU P,LIU Z,et al.Contrastive clustering[C]//Procee-dings of the AAAI Conference on Artificial Intelligence.2021:8547-8555. [11]DO K,TRAN T,VENKATESH S.Clustering by maximizingmutual information across views[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:9928-9938. [12]DANG Z,DENG C,YANG X,et al.Nearest neighbor matching for deep clustering[C]//Proceedings of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.2021:13693-13702. [13]ZHANG R,ISOLA P,EFROS A A.Colorful image colorization[C]//European Conference on Computer Vision.Cham:Sprin-ger,2016:649-666. [14]GIDARIS S,SINGH P,KOMODAKIS N.Unsupervised representation learning by predicting image rotations[J].arXiv:1803.07728,2018. [15]PATHAK D,KRAHENBUHL P,DONAHUE J,et al.Context encoders:Feature learning by inpainting[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:2536-2544. [16]VAN GANSBEKE W,VANDENHENDE S,GEORGOULIS S,et al.Scan:Learning to classify images without labels[C]//European Conference on Computer Vision.Cham:Springer,2020:268-285. [17]JI P,ZHANG T,LI H,et al.Deep subspace clustering networks[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:23-32. [18]YAMAGUCHI M,IRIE G,KAWANISHI T,et al.Subspacestructure-aware spectral clustering for robust subspace clustering[C]//Proceedings of the IEEE/CVF International Confe-rence on Computer Vision.2019:9875-9884. [19]LAW M T,URTASUN R,ZEMEL R S.Deep spectral clustering learning[C]//International Conference on Machine Lear-ning.PMLR,2017:1985-1994. [20]YANG X,DENG C,ZHENG F,et al.Deep spectral clusteringusing dual autoencoder network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:4066-4075. [21]ZHONG H,WU J,CHEN C,et al.Graph contrastive clustering[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:9224-9233. [22]WU J,LONG K,WANG F,et al.Deep comprehensive correlation mining for image clustering[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:8150-8159. [23]CHANG J,WANG L,MENG G,et al.Deep adaptive imageclustering[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.2017:5879-5887. [24]MAHON L,LUKASIEWICZT.Selective Pseudo-Label Clustering[C]//German Conference on Artificial Intelligence (Künstliche Intelligenz).Cham:Springer,2021:158-178. [25]GUPTA D,RAMJEE R,KWATRA N,et al.Unsupervised clustering using pseudo-semi-supervised learning[C]//International Conference on Learning Representations.2020. [26]PARK S,HAN S,KIM S,et al.Improving unsupervised image clustering with robust learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:12278-12287. [27]LOWE D G.Object recognition from local scale-invariant fea-tures[C]//Proceedings of the seventh IEEE International Conference on Computer Vision.1999:1150-1157. [28]DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]//2005 IEEEComputer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).2005:886-893. [29]VINCENT P,LAROCHELLE H,BENGIO Y,et al.Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th International Conference on Machine Learning.2008:1096-1103. [30]DONAHUE J,SIMONYAN K.Large scale adversarial representation learning[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems.2019:10542-10552. [31]NOROOZI M,PIRSIAVASH H,FAVARO P.Representationlearning by learning to count[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:5898-5906. [32]BACHMAN P,HJELM R D,BUCHWALTER W.Learningrepresentations by maximizing mutual information across views[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems.2019:15535-15545. [33]HADSELL R,CHOPRA S,LECUN Y.Dimensionality reduction by learning an invariant mapping[C]//2006 IEEE Compu-ter Society Conference on Computer Vision and Pattern Recognition (CVPR'06).IEEE,2006:1735-1742. [34]GRILL J B,STRUB F,ALTCHÉ F,et al.Bootstrap your own latent-a new approach to self-supervised learning[J].Advances in Neural Information Processing Systems,2020,33:21271-21284. [35]LI J,ZHOU P,XIONG C,et al.Prototypical Contrastive Lear-ning of Unsupervised Representations[C]//ICLR.2021. [36]GUO Y,XU M,LI J,et al.HCSC:Hierarchical Contrastive Selective Coding[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:9706-9715. [37]SAJJADI M,JAVANMARDI M,TASDIZEN T.Regularization with stochastic transformations and perturbations for deep semi-supervised learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.2016:1171-1179. [38]OORD A,LI Y,VINYALS O.Representation learning with contrastive predictive coding[J].arXiv:1807.03748,2018. [39]CHAPELLE O,SCHOLKOPF B,ZIEN A.Semi-supervisedlearning[J].IEEE Transactions on Neural Networks,2009,20(3):542-542. [40]SOHN K,BERTHELOT D,CARLINI N,et al.Fixmatch:Simplifying semi-supervised learning with consistency and confidence[J].Advances in Neural Information Processing Systems,2020,33:596-608. [41]HU Z,YANG Z,HU X,et al.Simple:similar pseudo label exploitation for semi-supervised classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:15099-15108. [42]ARAZO E,ORTEGO D,ALBERT P,et al.Pseudo-labeling and confirmation bias in deep semi-supervised learning[C]//2020 International Joint Conference on Neural Networks(IJCNN).IEEE,2020:1-8. [43]TARVAINEN A,VALPOLA H.Mean teachers are better role models:Weight-averaged consistency targets improve semi-supervised deep learning results[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:1195-1204. [44]LEE D H.Pseudo-label:The simple and efficient semi-supervised learning method for deep neural networks[C]//Workshop on Challenges in Representation Learning.2013:896. [45]BERTHELOT D,CARLINI N,GOODFELLOW I,et al.Mixmatch:A holistic approach to semi-supervised learning[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems.2019:5049-5059. [46]BERTHELOT D,CARLINI N,CUBUK E D,et al.Remix-match:Semi-supervised learning with distribution alignment and augmentation anchoring[J].arXiv:1911.09785,2019. [47]SELLARS P,AVILES-RIVERO A I,SCHÖNLIEB C B.Laplacenet:A hybrid energy-neural model for deep semi-supervised classification[J].arXiv:2106.04527,2021. [48]ZHANG B,WANG Y,HOU W,et al.Flexmatch:Boostingsemi-supervised learning with curriculum pseudo labeling[J].Advances in Neural Information Processing Systems,2021,34:18408-18419. [49]CHAPELLE O,SCHOLKOPF B,ZIEN A.Semi-supervisedlearning[J].IEEE Transactions on Neural Networks,2009,20(3):542-542. [50]ZHU X,GOLDBERG A B.Introduction to semi-supervisedlearning[J].Synthesis Lectures on Artificial Intelligence and Machine Learning,2009,3(1):1-130. [51]JOHNSON J,DOUZE M,JÉGOU H.Billion-scale similaritysearch with gpus[J].IEEE Transactions on Big Data,2019,7(3):535-547. [52]CUBUK E D,ZOPH B,SHLENS J,et al.Randaugment:Practical automated data augmentation with a reduced search space[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2020:702-703. [53]HUANG J,GONG S,ZHU X.Deep semantic clustering by partition confidence maximisation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:8849-8858. [54]ZHOU D,BOUSQUET O,LAL T,et al.Learning with local and global consistency[C]//Proceedings of the 16th International Conference on Neural Information Processing Systems.2003:321-328. [55]ZHANG H,CISSE M,DAUPHIN Y N,et al.mixup:Beyondempirical risk minimization[J].arXiv:1710.09412,2017. [56]HE K,SUN J.Convolutional neural networks at constrainedtime cost[C]//Proceedings of the IEEE Conference on Compu-ter Vision and Pattern Recognition.2015:5353-5360. [57]KRIZHEVSKY A,HINTON G.Learning multiple layers of features from tiny images[J/OL].Handbook of Systemic Autoimmune Diseases,2009,1(4).https://scholar.google.com/scho-lar?hl=zh-CN&as_sdt=0%2C5&q=Learning+multiple+la-yers+of+features+from+tiny+image&btnG=. [58]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014. [59]WANG F,JIANG M,QIAN C,et al.Residual attention network for image classification[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2017:3156-3164. [60]DEVRIES T,TAYLOR G W.Improved regularization of convolutional neural networks with cutout[J].arXiv:1708.04552,2017. [61]MCDAID A F,GREENE D,HURLEY N.Normalized mutual information to evaluate overlapping community finding algorithms[J].arXiv:1110.2515,2011. [62]HUBERT L,ARABIE P.Comparing partitions[J].Journal of Classification,1985,2(1):193-218. [63]MACQUUEN J B.Some methods for classification and analysis of multivariate observation[C]//Proceedings of the 5th Berkley Symposium on Mathematical Statistics and Probability.1967:281-297. [64]ZELNIK-MANOR L,PERONA P.Self-tuning spectral cluste-ring[C]//Proceedings of the 17th International Conference on Neural Information Processing Systems.2004:1601-1608. [65]CAI D,HE X,WANG X,et al.Locality preserving nonnegative matrix factorization[C]//21st International Joint Conference on Artificial Intelligence(IJCAI 2009).2009:1010-1015. [66]NG A.Sparse autoencoder[J].CS294A Lecture Notes,2011,72(2011):1-19. [67]VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(12):3371-3408. [68]BENGIO Y,LAMBLIN P,POPOVICI D,et al.Greedy layer-wise training of deep networks[C]//Proceedings of the 19th International Conference on Neural Information Processing Systems.2006:153-160. [69]RADFORD A,METZ L,CHINTALA S.Unsupervised representation learning with deep convolutional generative adversarial networks[J].arXiv:1511.06434,2015. [70]KINGMA D P,WELLING M.Auto-encoding variational bayes[J].arXiv:1312.6114,2013. [71]HAEUSSER P,PLAPP J,GOLKOV V,et al.Associative deep clustering:Training a classification network with no labels[C]//German Conference on Pattern Recognition.Cham:Springer,2018:18-32. [72]CARON M,BOJANOWSKI P,JOULIN A,et al.Deep cluste-ring for unsupervised learning of visual features[C]//Procee-dings of the European Conference on Computer Vision (ECCV).2018:132-149. [73]CHANG J,GUO Y,WANG L,et al.Deep discriminative clustering analysis[J].arXiv:1905.01681,2019. |
[1] | HE Xi, HE Ke-tai, WANG Jin-shan, LIN Shen-wen, YANG Jing-lin, FENG Yu-chao. Analysis of Bitcoin Entity Transaction Patterns [J]. Computer Science, 2022, 49(6A): 502-507. |
[2] | LIU Yang, ZHENG Wen-ping, ZHANG Chuan, WANG Wen-jian. Local Random Walk Based Label Propagation Algorithm [J]. Computer Science, 2022, 49(10): 103-110. |
[3] | CHU Jie, ZHANG Zheng-jun, TANG Xin-yao, HUANG Zhen-sheng. Label Propagation Algorithm Based on Weighted Samples and Consensus-rate [J]. Computer Science, 2021, 48(3): 214-219. |
[4] | KANG Yan, KOU Yong-qi, XIE Si-yu, WANG Fei, ZHANG Lan, WU Zhi-wei, LI Hao. Deep Clustering Model Based on Fusion Variational Graph Attention Self-encoder [J]. Computer Science, 2021, 48(11A): 81-87. |
[5] | DAI Cai-yan, CHEN Ling, HU Kong-fa. Algorithm for Mining Bipartite Network Based on Incremental Modularity [J]. Computer Science, 2018, 45(6A): 442-446. |
[6] | LIU Su-qi, BAI Guang-wei and SHEN Hang. Taxonomy Construction Based on User Self-describing Tags [J]. Computer Science, 2016, 43(7): 224-229. |
[7] | XU Wei, LIN Bo-gang, LIN Si-juan and YANG Yang. Assessment of User Influence in Social Networks Based on Multi-label Propagation [J]. Computer Science, 2016, 43(10): 135-140. |
[8] | MA Jie-liang, HAN Lu, PAN Zhen-zhen and SONG Yan. Label Propagation Algorithm Based on Community Core for Community Detection [J]. Computer Science, 2015, 42(1): 119-121. |
[9] | YANG Ge-lan,JIN Hui-xia,MENG Ling-zhong and ZHU Xing-hui. Graph-based Semi-supervised Dimensionality Reduction Algorithm [J]. Computer Science, 2014, 41(4): 280-282. |
[10] | . Study on Label Propagation Based Community Detection Algorithm for Social Semantic Network [J]. Computer Science, 2013, 40(2): 53-57. |
[11] | . Research on Sentiment Classification of Collaborative Learning Based on Emotion Words and Sentiment Words [J]. Computer Science, 2012, 39(12): 245-248. |
|