{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T16:46:13Z","timestamp":1758041173620,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032045577"},{"type":"electronic","value":"9783032045584"}],"license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04558-4_42","type":"book-chapter","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:16:48Z","timestamp":1757589408000},"page":"521-532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["XOOD: A Self-supervised Algorithm for\u00a0Detecting Out-of-Distribution Data for\u00a0Image Classification"],"prefix":"10.1007","author":[{"given":"Frej","family":"Berglind","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magesh","family":"Rajasekaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Saiful Islam","family":"Sajol","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haron","family":"Temam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Supratik","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamalika","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sricharan","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kumar","family":"Kallurupalli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"issue":"7553","key":"42_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Bishop, C.M., et\u00a0al.: Neural Networks for Pattern Recognition. Oxford university press (1995)","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.: DeepSat: a learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1\u201310 (2015)","DOI":"10.1145\/2820783.2820816"},{"issue":"1","key":"42_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/MC.2011.230","volume":"45","author":"S Iyengar","year":"2011","unstructured":"Iyengar, S., et al.: Toward more precise radiotherapy treatment of lung tumors. Computer 45(1), 59\u201365 (2011)","journal-title":"Computer"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Sharma, G., Alsaedi, R., Busch, C., Mukhopadhyay, S.: The complete visibility problem for fat robots with lights. In: Proceedings of the 19th International Conference on Distributed Computing and Networking, pp. 1\u20134 (2018)","DOI":"10.1145\/3154273.3154319"},{"key":"42_CR6","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 (2019). https:\/\/openreview.net\/"},{"key":"42_CR7","unstructured":"Ovadia, Y., et al.: Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"42_CR8","unstructured":"Wilson, A.G., Izmailov, P.: Bayesian deep learning and a probabilistic perspective of generalization. In: Advances in Neural Information Processing Systems, vol. 33, pp. 4697\u20134708 (2020)"},{"key":"42_CR9","unstructured":"Sastry, C.S., Oore, S.: Detecting out-of-distribution examples with gram matrices. In: International Conference on Machine Learning, pp. 8491\u20138501. PMLR (2020)"},{"key":"42_CR10","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"42_CR11","unstructured":"Sun, Y., Guo, C., Li, Y.: React: Out-of-distribution detection with rectified activations. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"issue":"4","key":"42_CR12","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","volume":"87","author":"IK Yeo","year":"2000","unstructured":"Yeo, I.K., Johnson, R.A.: A new family of power transformations to improve normality or symmetry. Biometrika 87(4), 954\u2013959 (2000)","journal-title":"Biometrika"},{"key":"42_CR13","unstructured":"https:\/\/github.com\/MdSaifulIslamSajol\/xood-icann\/"},{"key":"42_CR14","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24-26 April 2017, Conference Track Proceedings (2017). https:\/\/openreview.net"},{"key":"42_CR15","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings (2018). https:\/\/openreview.net"},{"key":"42_CR16","unstructured":"Yang, J., Zhou, K., Li, Y., et al.: Generalized out-of-distribution detection: a survey. arXiv preprint arXiv:2110.11334 (2021)"},{"key":"42_CR17","unstructured":"Zhang, J., et\u00a0al.: OpenOOD v1. 5: enhanced benchmark for out-of-distribution detection. arXiv preprint arXiv:2306.09301 (2023)"},{"key":"42_CR18","unstructured":"Mohseni, S., Vahdat, A., Yadawa, J.: Shifting transformation learning for out-of-distribution detection (2021)"},{"key":"42_CR19","unstructured":"Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"42_CR20","unstructured":"Sehwag, V., Chiang, M., Mittal, P.: SSD: a unified framework for self-supervised outlier detection. arXiv preprint arXiv:2103.12051 (2021)"},{"key":"42_CR21","first-page":"11839","volume":"33","author":"J Tack","year":"2020","unstructured":"Tack, J., Mo, S., Jeong, J., Shin, J.: CSI: novelty detection via contrastive learning on distributionally shifted instances. Adv. Neural. Inf. Process. Syst. 33, 11839\u201311852 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"42_CR22","unstructured":"Larson, S., Lim, Y.Y.G., Ai, Y., Kuang, D., Leach, K.: Evaluating out-of-distribution performance on document image classifiers. arXiv preprint arXiv:2210.07448 (2022)"},{"key":"42_CR23","first-page":"21464","volume":"33","author":"W Liu","year":"2020","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. Adv. Neural. Inf. Process. Syst. 33, 21464\u201321475 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"42_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-77967-2_13","volume-title":"Computational Science \u2013 ICCS 2021","author":"T Walkowiak","year":"2021","unstructured":"Walkowiak, T., Szyc, K., Maciejewski, H.: On validity of extreme value theory-based parametric models for out-of-distribution detection. In: Paszynski, M., Kranzlm\u00fcller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2021. LNCS, vol. 12744, pp. 142\u2013155. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77967-2_13"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Yau, S.S., Davulcu, H., Mukhopadhyay, S., Huang, D., Yao, Y.: Adaptable situation-aware secure service-based (AS\/SUP 3\/) systems. In: Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 2005), pp. 308\u2013315. IEEE (2005)","DOI":"10.1109\/ISORC.2005.7"},{"issue":"4","key":"42_CR26","doi-asserted-by":"publisher","first-page":"59","DOI":"10.4018\/jwsr.2007100103","volume":"4","author":"SS Yau","year":"2007","unstructured":"Yau, S.S., Davulcu, H., Mukhopadhyay, S., Huang, D., Gong, H., Singh, P., Gelgi, F.: Automated situation-aware service composition in service-oriented computing. Int. J. Web Serv. Res. (IJWSR) 4(4), 59\u201382 (2007)","journal-title":"Int. J. Web Serv. Res. (IJWSR)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04558-4_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:16:57Z","timestamp":1757589417000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04558-4_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"ISBN":["9783032045577","9783032045584"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04558-4_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}