Xinghao Yang

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Codes & Data

Code Release

  1. BU-SPO: Semantic-preserving Adversarial Text Attacks.
  2. BU-MHS: Bigram and Unigram Based Text Attack via Adaptive Monotonic Heuristic Search.
  3. TAA: Targeted Attention Attack on Deep Learning Models in Road Sign Recognition.
  4. BESA: BERT-based Simulated Annealing for Adversarial Text Attacks.
  5. TCCANet: Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition.

A Survey on Canonical Correlation Analysis -- Codes and Data

  1. S. Eleftheriadis, O. Rudovic, and M. Pantic, “Discriminative shared gaussian processes for multiview and view-invariant facial expression recognition,” IEEE Trans. Image Process., vol. 24, no. 1, pp. 189–204, 2015. [SFEW]
  2. X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 6526–6534. [Python Code] [KITTI object detection benchmark]
  3. Y. Luo, D. Tao, K. Ramamohanarao, C. Xu, and Y. Wen, “Tensor canonical correlation analysis for multi-view dimension reduction,” IEEE Trans. Knowl. Data Eng., vol. 27, no. 11, pp. 3111–3124, 2015. [Matlab Code] [SecStr data set, Internet advertisement, NUS-WIDE]
  4. A. Klami, S. Virtanen, and S. Kaski, “Bayesian canonical correlation analysis,” J. Mach Learn. Res., vol. 14, no. Apr, pp. 965–1003, 2013. [R Code, Mulan Library]
  5. G. Andrew, R. Arora, J. Bilmes, and K. Livescu, “Deep canonical correlation analysis,” in Proc. Int. Conf. Mach. Learn. (ICML), 2013, pp. 1247–1255. [Python Code] [MNIST, Speech Data XRMB]
  6. A. Sharma, A. Kumar, H. Daume, and D. W. Jacobs, “Generalized multiview analysis: A discriminative latent space,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2012, pp. 2160–2167. [Pascal VOC 2007]
  7. D. R. Hardoon and J. Shawe-Taylor, “Sparse canonical correlation analysis,” Mach. Learn., vol. 83, no. 3, pp. 331–353, 2011. [EuroParl, JRC-Acquis]
  8. S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” science, vol. 290, no. 5500, pp. 2323–2326, 2000. [Matlab Code] [Gallery]
  9. J. Rupnik and J. Shawe-Taylor, “Multi-view canonical correlation analysis,” in Proc. Conf. Data Min. Data Warehouses, 2010, pp. 1–4. [EuroParl]
  10. S. Virtanen, A. Klami, and S. Kaski, “Bayesian cca via group sparsity,” in Proc. Int. Conf. Mach. Learn. (ICML). Omnipress, 2011, pp. 457–464. [R Code] [Mulan Library]
  11. X. Yang, W. Liu, D. Tao, and J. Cheng, “Canonical correlation analysis networks for two-view image recognition,” Inf. Sci., vol. 385, pp. 338–352, 2017. [ETH-80, USPS]
  12. F. R. Bach and M. I. Jordan, “Kernel independent component analysis,” J. Mach Learn. Res., vol. 3, no. Jul, pp. 1–48, 2002. [Matlab Code]
  13. D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonical correlation analysis: An overview with application to learning methods,” Neural Comput., vol. 16, no. 12, pp. 2639–2664, 2004. [Matlab Code]
  14. W. Zheng, X. Zhou, C. Zou, and L. Zhao, “Facial expression recognition using kernel canonical correlation analysis (kcca),” IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 233–238, 2006. [JAFFE database, "Pictures of Facial Affect" database]
  15. T. Sun, S. Chen, J. Yang, and P. Shi, “A novel method of combined feature extraction for recognition,” in Proc. IEEE Int. Conf. Data Mining (ICDM). IEEE, 2008, pp. 1043–1048. [WebKB database, ORL face database, multiple feature database]
  16. T. Sun, S. Chen, J. Yang, X. Hu, and P. Shi, “Discriminative canonical correlation analysis with missing samples,” in Proc. World Congr. Comput. Sci. Inf. Eng, vol. 6. IEEE, 2009, pp. 95–99. [WebKB database, multiple feature database]
  17. L. Sun, S. Ji, and J. Ye, “Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 1, pp. 194–200, Jan 2011. [BDGP database, Scene database]
  18. D. M. Witten and R. J. Tibshirani, “Extensions of sparse canonical correlation analysis with applications to genomic data,” Statist. Appl. Genet. Mol. Biol., vol. 8, no. 1, pp. 1–27, 2009. [R Code] [DLBCL database]
  19. D. M. Witten, R. Tibshirani, and T. Hastie, “A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis,” Biostatistics, vol. 10, no. 3, pp. 515–534, 2009. [R Code]
  20. T. Sun and S. Chen, “Locality preserving cca with applications to data visualization and pose estimation,” Image Vis. Comput., vol. 25, no. 5, pp. 531–543, 2007. [COIL-20 database]
  21. F. Wang and D. Zhang, “A new locality-preserving canonical correlation analysis algorithm for multi-view dimensionality reduction,” Neural Process. Lett., vol. 37, no. 2, pp. 135–146, 2013. [UCI Multiple Feature database, ORL face database, Yale face database]
  22. J. Rupnik, P. Skraba, J. Shawe-Taylor, and S. Guettes, “A comparison of relaxations of multiset cannonical correlation analysis and applications,” arXiv preprint arXiv:1302.0974, 2013. [EuroParl]
  23. A. Lu, W. Wang, M. Bansal, K. Gimpel, and K. Livescu, “Deep multilingual correlation for improved word embeddings,” in Conf. North Amer. Chapt. Assoc. Comput. Linguist.: Human Language Technol. (NAACL-HLT), 2015, pp. 250–256. [WMT 2011 English and German]
  24. W. Wang, R. Arora, K. Livescu, and J. A. Bilmes, “Unsupervised learning of acoustic features via deep canonical correlation analysis,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), 2015, pp. 4590–4594. [Matlab Code] [Speech Data XRMB]
  25. W. Wang, R. Arora, K. Livescu, and J. Bilmes, “On deep multi-view representation learning,” in Proc. Int. Conf. Mach. Learn. (ICML), 2015, pp. 1083–1092. [Python Code] [MNIST, Speech Data XRMB, WMT2011]
  26. W. Wang, R. Arora, K. Livescu, and N. Srebro, “Stochastic optimization for deep cca via nonlinear orthogonal iterations,” in 53rd Annu. Conf. Commun, Control, Comput., Sep. 2015, pp. 688–695. [Speech Data XRMB, MNIST]
  27. X. Yang and W. Liu, “Multiple scale canonical correlation analysis networks for two-view object recognition,” in Proc. Int. Conf. Neural Inform. Process (ICONIP), 2017, pp. 325–334. [ETH-80]
  28. S. Chandar, M. M. Khapra, H. Larochelle, and B. Ravindran, “Correlational neural networks,” Neural Comput., vol. 28, no. 2, pp. 257–285, 2016. [Python Code] [Crosslingual data, MOSES]
  29. M. B. Blaschko, C. H. Lampert, and A. Gretton, “Semi-supervised laplacian regularization of kernel canonical correlation analysis,” in Proc. Eur. Conf. Mach. Learn. Knowl. Disc. Databases (ECML/PKDD), 2008, pp. 133–145. [UIUC-ISD]
  30. S.-Y. Huang, M.-H. Lee, and C. K. Hsiao, “Nonlinear measures of association with kernel canonical correlation analysis and applications,” J. Stat. Plan. Infer., vol. 139, no. 7, pp. 2162–2174, 2009. [Pen-Based Recognition of Handwritten Digits]
  31. X. Zhu, Z. Huang, H. T. Shen, J. Cheng, and C. Xu, “Dimensionality reduction by mixed kernel canonical correlation analysis,” Pattern Recognit., vol. 45, no. 8, pp. 3003–3016, 2012. [Ads, WebKB database]
  32. Y. Peng, D. Zhang, and J. Zhang, “A new canonical correlation analysis algorithm with local discrimination,” Neural Process. Lett., vol. 31, no. 1, pp. 1–15, 2010. [ORL, Yale face database, AR]
  33. Y. Shin and C. Park, “Analysis of correlation based dimension reduction methods,” Int. J. Ap. Mat. Com-Pol, vol. 21, no. 3, pp. 549–558, 2011. [Multiple Features Dataset, ISOLET, Letimg, Pendigit, Optdigit, Semdigit]
  34. W. Zuobin, M. Kezhi, and G.-W. Ng, “Effective feature fusion for pattern classification based on intra-class and extra-class discriminative correlation analysis,” in Proc. Int. Conf. Inf. Fusion (FUSION). IEEE, 2017, pp. 1–8. [D-case, SDUMLA-HMT, AR]
  35. X. Chen, L. Han, and J. Carbonell, “Structured sparse canonical correlation analysis,” in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS), 2012, pp. 199–207. [KEGG]
  36. D. Lin, J. Zhang, J. Li, V. D. Calhoun, H.-W. Deng, and Y.-P. Wang, “Group sparse canonical correlation analysis for genomic data integration,” BMC Bioinf., vol. 14, no. 1, p. 245, 2013. [Matlab Code] [GSE6109, GSE4290, NCI60]
  37. D. Lin, V. D. Calhoun, and Y.-P. Wang, “Correspondence between fmri and snp data by group sparse canonical correlation analysis,” Med. Image Anal., vol. 18, no. 6, pp. 891–902, 2014. [SPM, SZGene]
  38. M. Kanai, R. Togo, T. Ogawa, and M. Haseyama, “Aesthetic quality assessment of images via supervised locality preserving cca,” in IEEE Glob. Conf. Consum. Electron. (GCCE), 2017, pp. 1–2. [AVA]
  39. Y. Tian, L. Sigal, F. De la Torre, and Y. Jia, “Canonical locality preserving latent variable model for discriminative pose inference,” Image Vis. Comput., vol. 31, no. 3, pp. 223–230, 2013. [CMU]
  40. L. Sun, S. Ji, and J. Ye, “A least squares formulation for canonical correlation analysis,” in Proc. Int. Conf. Mach. Learn. (ICML), 2008, pp. 1024–1031. [FlyExpress database ]
  41. C. O. Sakar and O. Kursun, “Discriminative feature extraction by a neural implementation of canonical correlation analysis,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 1, pp. 164–176, 2017. [CK+ Database, COIL-100, FLICKR]
  42. R. Arora and K. Livescu, “Multi-view learning with supervision for transformed bottleneck features,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), 2014, pp. 2499–2503. [Speech Data XRMB]
  43. X. Yang, W. Liu, D. Tao, J. Cheng, and S. Li, “Multiview canonical correlation analysis networks for remote sensing image recognition,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1855–1859, 2017. [RSSCN7]
  44. T.-K. Kim, S.-F. Wong, and R. Cipolla, “Tensor canonical correlation analysis for action classification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2007, pp. 1–8. [Cambridge Hand Gesture Data set, KTH action data set]
  45. C. Archambeau and F. R. Bach, “Sparse probabilistic projections,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., 2009, pp. 73–80. [C# Code]
  46. A. Klami, S. Virtanen, and S. Kaski, “Bayesian exponential family projections for coupled data sources,” arXiv preprint arXiv:1203.3489, 2012. [UCI SPECT, Allmovie]
  47. F. Yan and K. Mikolajczyk, “Deep correlation for matching images and text,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 3441–3450. [Flickr8K, Flickr30K, IAPR TC-12]
  48. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proc. Int. Conf. Mach. Learn. (ICML), 2008, pp. 1096–1103. [Variations of the MNIST]
  49. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2012, pp. 1097–1105. [Python Code] [ImageNet]
  50. Y. Yamanishi, J.-P. Vert, A. Nakaya, and M. Kanehisa, “Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis,” Bioinformatics, vol. 19, pp. i323–i330, 2003. [KEGG/LIGAND, KEGG/BRITE, ExpressDB, KEGG Pathway]
  51. M. Kuss and T. Graepel, “The geometry of kernel canonical correlation analysis,” 2003. [Iris Data Set ]
  52. G. Baudat and F. Anouar, “Generalized discriminant analysis using a kernel approach,” Neural Comput., vol. 12, no. 10, pp. 2385–2404, 2000. [Iris Data Set , Seed classification]
  53. A.-R. Mohammadi-Nejad, G.-A. Hossein-Zadeh, and H. Soltanian-Zadeh, “Structured and sparse canonical correlation analysis as a brain-wide multi-modal data fusion approach,” IEEE Trans. Med. Imag., vol. 36, no. 7, pp. 1438–1448, 2017. [ADNI]
  54. W. Zheng, “Multichannel eeg-based emotion recognition via group sparse canonical correlation analysis,” IEEE Trans. Cogn. Devel. Syst., vol. 9, no. 3, pp. 281–290, 2017. [SJTU Emotion EEG Dataset (SEED)]
  55. A. Gossmann, P. Zille, V. Calhoun, and Y.-P. Wang, “Fdr-corrected sparse canonical correlation analysis with applications to imaging genomics,” IEEE Trans. Med. Imag., 2018. [R Code] [Philadelphia Neurodevelopmental Cohort]
  56. X. Hao, C. Li, L. Du, X. Yao, J. Yan, S. L. Risacher, A. J. Saykin, L. Shen, D. Zhang, M. W. Weiner et al., “Mining outcome-relevant brain imaging genetic associations via three-way sparse canonical correlation analysis in alzheimers disease,” Sci. Rep-UK, vol. 7, p. 44272, 2017. [ADNI]
  57. Z. Zhang, M. Zhao, and T. W. Chow, “Binary-and multi-class group sparse canonical correlation analysis for feature extraction and classification,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 10, pp. 2192–2205, 2013. [UCI data sets, ETH-80, BSDS500]
  58. S. Dudoit, J. Fridlyand, and T. P. Speed, “Comparison of discrimination methods for the classification of tumors using gene expression data,” J. Am. Stat. Assoc., vol. 97, no. 457, pp. 77–87, 2002. [Lymphoma, Leukemia, NCI60]
  59. R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, “Class prediction by nearest shrunken centroids, with applications to dna microarrays,” Stat. Sci., pp. 104–117, 2003. [lymphoma, NCI60]
  60. N. Parikh, S. Boyd et al., “Proximal algorithms,” Found. Trends Optim., vol. 1, no. 3, pp. 127–239, 2014. [Matlab Code]
  61. S. R. Becker, E. J. Candès, and M. C. Grant, “Templates for convex cone problems with applications to sparse signal recovery,” Math. Program. Comput., vol. 3, no. 3, p. 165, 2011. [Matlab Code]
  62. T.-K. Kim and R. Cipolla, “Canonical correlation analysis of video volume tensors for action categorization and detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 8, pp. 1415–1428, 2009. [Cambridge Hand Gesture Data set, KTH action data set]
  63. H. Huang, H. He, X. Fan, and J. Zhang, “Super-resolution of human face image using canonical correlation analysis,” Pattern Recognit., vol. 43, no. 7, pp. 2532–2543, 2010. [CAS-PEAL database]
  64. W. De Clercq, A. Vergult, B. Vanrumste, W. Van Paesschen, and S. Van Huffel, “Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2583–2587, 2006. [Matlab Code]
  65. A. Vinokourov, N. Cristianini, and J. Shawe-Taylor, “Inferring a semantic representation of text via cross-language correlation analysis,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2003, pp. 1497–1504. [ Hansard]
  66. A. Haghighi, P. Liang, T. Berg-Kirkpatrick, and D. Klein, “Learning bilingual lexicons from monolingual corpora,” Proc. ACL-08: HLT, pp. 771–779, 2008. [English and Spanish Europarl corpus]
  67. A. A. Nielsen, “Multiset canonical correlations analysis and multi-spectral, truly multitemporal remote sensing data,” IEEE Trans. Image Process., vol. 11, no. 3, pp. 293–305, 2002. [Landsat TM data]
  68. Y.-H. Yuan, Q.-S. Sun, Q. Zhou, and D.-S. Xia, “A novel multiset integrated canonical correlation analysis framework and its application in feature fusion,” Pattern Recognit., vol. 44, no. 5, pp. 1031–1040, 2011. [CENPARMI handwritten numerals database, Multiple Features Dataset]
  69. M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi, “Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 9, pp. 1984–1996, 2016. [Matlab Code] [AR]
  70. Y.-O. Li, T. Adali, W. Wang, and V. D. Calhoun, “Joint blind source separation by multiset canonical correlation analysis,” IEEE Trans. Signal Process., vol. 57, no. 10, pp. 3918–3929, 2009. [Matlab Code]
  71. A. Mandal and P. Maji, “Faroc: fast and robust supervised canonical correlation analysis for multimodal omics data,” IEEE Trans. Cybern., vol. 48, no. 4, pp. 1229–1241, 2018. [TCGA]
  72. L. Gao, L. Qi, E. Chen, and L. Guan, “Discriminative multiple canonical correlation analysis for information fusion,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 1951–1965, 2018. [MNIST, eNTERFACE]
  73. I. Huopaniemi, T. Suvitaival, J. Nikkilä, M. Oreˇ siˇ c, and S. Kaski, “Multivariate multi-way analysis of multi-source data,” Bioinformatics, vol. 26, no. 12, pp. i391–i398, 2010. [R Code]
  74. M. A. Nicolaou, V. Pavlovic, and M. Pantic, “Dynamic probabilistic cca for analysis of affective behavior and fusion of continuous annotations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 7, pp. 1299–1311, 2014. [ SEMAINE database, MMI database]
  75. Y. Zhou, H. Lu, and Y.-m. Cheung, “Bilinear probabilistic canonical correlation analysis via hybrid concatenations.” in Proc. 31st AAAI Conf. Artif. Intell., 2017, pp. 2949–2955. [CUHK face-sketch database (CUFS)]
  76. V. N. Murthy, S. Maji, and R. Manmatha, “Automatic image annotation using deep learning representations,” in Proc. 5th ACM Int. Conf. Multimedia Retr., 2015, pp. 603–606. [MSR Action3D, UTD-MHAD, KARD dataset, SBU Kinect Interaction dataset]
  77. J. Shao, L. Wang, Z. Zhao, A. Cai et al., “Deep canonical correlation analysis with progressive and hypergraph learning for cross-modal retrieval,” Neurocomputing, vol. 214, pp. 618–628, 2016. [Pascal, NUS-WIDE]
  78. A. Benton, H. Khayrallah, B. Gujral, D. A. Reisinger, S. Zhang, and R. Arora, “Deep generalized canonical correlation analysis,” arXiv preprint arXiv:1702.02519, 2017. [Python Code] [Speech Data XRMB, Twitter Users]
  79. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013. [Code and data: Word2vec website]
  80. R. Arora and K. Livescu, “Kernel cca for multi-view learning of acoustic features using articulatory measurements,” in Symp. Mach. Learn. Speech Lang. Process. (MLSLP), 2012. [Speech Data XRMB]
  81. G. Lisanti, I. Masi, and A. Del Bimbo, “Matching people across camera views using kernel canonical correlation analysis,” in Proc. Int. Conf. Distrib. Smart Cameras (ICDSC), 2014, p. 10. [Matlab Code] [VIPeR, PRID]
  82. G. Lisanti, S. Karaman, and I. Masi, “Multichannel-kernel canonical correlation analysis for cross-view person reidentification,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 13, no. 2, p. 13, 2017. [VIPeR, PRID, PRID 450s, CUHK01]
  83. S. Mehrkanoon and J. A. Suykens, “Regularized semipaired kernel cca for domain adaptation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 7, pp. 3199–3213, 2018. [multiple feature database, Office benchmark, IXMAS Actions]
  84. O. Arandjelovi´ c, “Discriminative extended canonical correlation analysis for pattern set matching,” Mach. Learn., vol. 94, no. 3, pp. 353–370, 2014. [Films Face Databases]
  85. S. Sun, X. Xie, and M. Yang, “Multiview uncorrelated discriminant analysis,” IEEE Trans. Cybern., vol. 46, no. 12, pp. 3272–3284, 2016. [Matlab Code]
  86. N. E. D. Elmadany, Y. He, and L. Guan, “Multiview learning via deep discriminative canonical correlation analysis,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), 2016, pp. 2409–2413. [MNIST]
  87. X. Huang, B. Zhang, H. Qiao, and X. Nie, “Local discriminant canonical correlation analysis for supervised polsar image classification,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 11, pp. 2102–2106, 2017. [PolSAR dataset]
  88. Y.-H. Yuan and Q.-S. Sun, “Multiset canonical correlations using globality preserving projections with applications to feature extraction and recognition,” IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 6, pp. 1131–1146, 2014. [COIL-100, ETH-80, AR, Yale face database]
  89. Y.-H. Yuan and Q.-S. Sun, “Graph regularized multiset canonical correlations with applications to joint feature extraction,” Pattern Recognit., vol. 47, no. 12, pp. 3907–3919, 2014. [AR, Extend Yale-B, ORL, ETH-80]
  90. N. E. D. Elmadany, Y. He, and L. Guan, “Information fusion for human action recognition via biset/multiset globality locality preserving canonical correlation analysis,” IEEE Trans. Image Process., vol. 27, no. 11, pp. 5275–5287, 2018. [MSR Action3D, UTD-MHAD, KARD dataset, SBU Kinect Interaction dataset]
  91. J. Chen, G. Wang, and G. B. Giannakis, “Graph multiview canonical correlation analysis,” IEEE Trans. Signal Process., vol. 67, no. 11, pp. 2826–2838, 2019. [Twitter Users, Multiple Feature database, Extend Yale-B, MNIST]
  92. M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein, “SVCCA: Singular vector canonical correlation analysis for deep learning dynamics and interpretability,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2017, pp. 6076–6085. [Python Code] [Cifar-10]
  93. A. Morcos, M. Raghu, and S. Bengio, “Insights on representational similarity in neural networks with canonical correlation,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2018, pp. 5727–5736. [Python Code] [Cifar-10, WikiText-2, Penn Tree Bank]