乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T05:09:09Z","timestamp":1726117749909},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687984"},{"type":"electronic","value":"9783030687991"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68799-1_51","type":"book-chapter","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T08:03:53Z","timestamp":1614845033000},"page":"705-718","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Image Anomaly Detection by Aggregating Deep Pyramidal Representations"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1945-5737","authenticated-orcid":false,"given":"Pankaj","family":"Mishra","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5305-1520","authenticated-orcid":false,"given":"Claudio","family":"Piciarelli","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8425-6892","authenticated-orcid":false,"given":"Gian Luca","family":"Foresti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481\u2013490 (2019)","key":"51_CR1","DOI":"10.1109\/CVPR.2019.00057"},{"key":"51_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","volume":"60","author":"M Ahmed","year":"2016","unstructured":"Ahmed, M., Mahmood, A.N., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19\u201331 (2016)","journal-title":"J. Netw. Comput. Appl."},{"key":"51_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018, Part III. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"unstructured":"Ambrogioni, L., G\u00fc\u00e7l\u00fc, U., van Gerven, M.A., Maris, E.: The ernel mixture network: A nonparametric method for conditional density estimation of continuous random variables. arXiv preprint arXiv:1705.07111 (2017)","key":"51_CR4"},{"unstructured":"Antonie, M.L., Za\u00efane, O.R., Coman, A.: Application of data mining techniques for medical image classification. In: Proceedings of the Second International Conference on Multimedia Data Mining, MDMKDD 2001, pp. 94\u2013101. (2001)","key":"51_CR5"},{"unstructured":"Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 37\u201349 (2012)","key":"51_CR6"},{"doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD-a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9592\u20139600 (2019)","key":"51_CR7","DOI":"10.1109\/CVPR.2019.00982"},{"doi-asserted-by":"crossref","unstructured":"Bergmann, P., L\u00f6we, S., Fauser, M., Sattlegger, D., Steger, C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. In: International joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019)","key":"51_CR8","DOI":"10.5220\/0007364503720380"},{"key":"51_CR9","volume-title":"Mixture Density Networks","author":"CM Bishop","year":"1994","unstructured":"Bishop, C.M.: Mixture Density Networks. Aston University, Birmingham (1994)"},{"doi-asserted-by":"crossref","unstructured":"Cai, Q., Pan, Y., Yao, T., Yan, C., Mei, T.: Memory matching networks for one-shot image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4080\u20134088 (2018)","key":"51_CR10","DOI":"10.1109\/CVPR.2018.00429"},{"unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: A survey. CoRR abs\/1901.03407 (2019). http:\/\/arxiv.org\/abs\/1901.03407","key":"51_CR11"},{"issue":"3","key":"51_CR12","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 151\u20131558 (2009)","journal-title":"ACM Comput. Surv."},{"issue":"6","key":"51_CR13","doi-asserted-by":"publisher","first-page":"3832","DOI":"10.1109\/TIE.2014.2350451","volume":"62","author":"P Chen","year":"2015","unstructured":"Chen, P., Yang, S., McCann, J.A.: Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Trans. Ind. Electron. 62(6), 3832\u20133842 (2015)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"51_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-10925-7_1","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"L Deecke","year":"2019","unstructured":"Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., Kloft, M.: Image anomaly detection with generative adversarial networks. In: Berlingerio, M., Bonchi, F., G\u00e4rtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018, Part I. LNCS (LNAI), vol. 11051, pp. 3\u201317. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10925-7_1"},{"key":"51_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compind.2014.10.006","volume":"66","author":"SH Huang","year":"2015","unstructured":"Huang, S.H., Pan, Y.C.: Automated visual inspection in the semiconductor industry: a survey. Comput. Ind. 66, 1\u201310 (2015)","journal-title":"Comput. Ind."},{"key":"51_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part II. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)","key":"51_CR17"},{"unstructured":"Klushyn, A., Chen, N., Kurle, R., Cseke, B., van der Smagt, P.: Learning hierarchical priors in VAEs. In: Advances in Neural Information Processing Systems, vol. 32, pp. 2866\u20132875. Curran Associates, Inc. (2019). http:\/\/papers.nips.cc\/paper\/8553-learning-hierarchical-priors-in-vaes.pdf","key":"51_CR18"},{"unstructured":"Kumagai, A., Iwata, T., Fujiwara, Y.: Transfer anomaly detection by inferring latent domain representations. In: Advances in Neural Information Processing Systems, pp. 2467\u20132477 (2019)","key":"51_CR19"},{"issue":"11","key":"51_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"10","key":"51_CR21","doi-asserted-by":"publisher","first-page":"2050060","DOI":"10.1142\/S0129065720500604","volume":"30","author":"P Mishra","year":"2020","unstructured":"Mishra, P., Piciarelli, C., Foresti, G.L.: A neural network for image anomaly detection with deep pyramidal representations and dynamic routing. Int. J. Neural Syst. 30(10), 2050060 (2020)","journal-title":"Int. J. Neural Syst."},{"issue":"1","key":"51_CR22","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3390\/s18010209","volume":"18","author":"P Napoletano","year":"2018","unstructured":"Napoletano, P., Piccoli, F., Schettini, R.: Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 18(1), 209 (2018)","journal-title":"Sensors"},{"unstructured":"Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with pixelCNN decoders. In: Advances in Neural Information Processing Systems, pp. 4790\u20134798 (2016)","key":"51_CR23"},{"doi-asserted-by":"crossref","unstructured":"Perera, P., Nallapati, R., Xiang, B.: Ocgan: One-class novelty detection using GANs with constrained latent representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2898\u20132906 (2019)","key":"51_CR24","DOI":"10.1109\/CVPR.2019.00301"},{"issue":"6","key":"51_CR25","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1109\/TII.2018.2873237","volume":"15","author":"C Piciarelli","year":"2018","unstructured":"Piciarelli, C., Avola, D., Pannone, D., Foresti, G.L.: A vision-based system for internal pipeline inspection. IEEE Trans. Ind. Inf. 15(6), 3289\u20133299 (2018)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"11","key":"51_CR26","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/TCSVT.2008.2005599","volume":"18","author":"C Piciarelli","year":"2008","unstructured":"Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544\u20131554 (2008)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"51_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/978-3-030-30642-7_23","volume-title":"Image Analysis and Processing \u2013 ICIAP 2019","author":"C Piciarelli","year":"2019","unstructured":"Piciarelli, C., Mishra, P., Foresti, G.L.: Image anomaly detection with capsule networks and imbalanced datasets. In: Ricci, E., Rota Bul\u00f2, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019, Part I. LNCS, vol. 11751, pp. 257\u2013267. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30642-7_23"},{"unstructured":"Pidhorskyi, S., Almohsen, R., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. In: Advances in Neural Information Processing Systems, pp. 6822\u20136833 (2018)","key":"51_CR28"},{"unstructured":"Qin, X., Cao, L., Rundensteiner, E.A., Madden, S.: Scalable kernel density estimation-based local outlier detection over large data streams. In: EDBT, pp. 421\u2013432 (2019)","key":"51_CR29"},{"unstructured":"Ruff, L., et al.: Deep one-class classification. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4393\u20134402. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden (2018)","key":"51_CR30"},{"key":"51_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T., Shen, D. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"issue":"1","key":"51_CR32","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s11222-017-9793-z","volume":"29","author":"C Viroli","year":"2019","unstructured":"Viroli, C., McLachlan, G.J.: Deep Gaussian mixture models. Stat. Comput. 29(1), 43\u201351 (2019)","journal-title":"Stat. Comput."},{"doi-asserted-by":"crossref","unstructured":"Wulsin, D., Blanco, J., Mani, R., Litt, B.: Semi-supervised anomaly detection for EEG waveforms using deep belief nets. In: 2010 Ninth International Conference on Machine Learning and Applications, pp. 436\u2013441 (2010)","key":"51_CR33","DOI":"10.1109\/ICMLA.2010.71"},{"unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)","key":"51_CR34"},{"doi-asserted-by":"crossref","unstructured":"Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 665\u2013674. ACM, New York (2017)","key":"51_CR35","DOI":"10.1145\/3097983.3098052"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68799-1_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T09:46:13Z","timestamp":1614851173000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68799-1_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687984","9783030687991"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68799-1_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}