{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:48:56Z","timestamp":1782499736999,"version":"3.54.5"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032299239","type":"print"},{"value":"9783032299246","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-29924-6_25","type":"book-chapter","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:04:39Z","timestamp":1782497079000},"page":"325-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Plausible Visual Counterfactual Explanations in\u00a0Latent Space with\u00a0Normalizing Flows"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6282-8649","authenticated-orcid":false,"given":"\u0141ukasz","family":"Lenkiewicz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4964-0547","authenticated-orcid":false,"given":"Marcel","family":"Musia\u0142ek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2184-3096","authenticated-orcid":false,"given":"Oleksii","family":"Furman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4217-7712","authenticated-orcid":false,"given":"Maciej","family":"Zi\u0119ba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"25_CR1","unstructured":"Atad, M., et al.: Chexplaining in style: counterfactual explanations for chest X-rays using stylegan. arXiv preprint arXiv:2207.07553 (2022)"},{"key":"25_CR2","doi-asserted-by":"publisher","unstructured":"Atad, M., et al.: Counterfactual explanations for medical image classification and regression using diffusion autoencoder, vol. abs\/2408.01571 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2408.01571","DOI":"10.48550\/ARXIV.2408.01571"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Augustin, M., Boreiko, V., Croce, F., Hein, M.: Diffusion visual counterfactual explanations. In: Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)","DOI":"10.52202\/068431-0027"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Duong, T.D., Li, Q., Xu, G.: Ceflow: a robust and efficient counterfactual explanation framework for tabular data using normalizing flows. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 133\u2013144. Springer (2023)","DOI":"10.1007\/978-3-031-33377-4_11"},{"key":"25_CR5","unstructured":"Furman, O., Movsum-zada, U., Marszalek, P., Zi\u0119ba, M., \u015amieja, M.: Dicoflex: model-agnostic diverse counterfactuals with flexible control. arXiv preprint arXiv:2505.23700 (2025)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Hvilsh\u00f8j, F., Iosifidis, A., Assent, I.: ECINN: efficient counterfactuals from invertible neural networks. arXiv preprint arXiv:2103.13701 (2021)","DOI":"10.5244\/C.35.159"},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Jeanneret, G., Simon, L., Jurie, F.: Diffusion models for counterfactual explanations. In: Computer Vision - ACCV 2022 - 16th Asian Conference on Computer Vision, Macao, China, December 4-8, 2022, Proceedings, Part VII. Lecture Notes in Computer Science, vol. 13847, pp. 219\u2013237. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-26293-7_14","DOI":"10.1007\/978-3-031-26293-7_14"},{"key":"25_CR8","unstructured":"Joshi, S., Koyejo, O., Vijitbenjaronk, W., Kim, B., Ghosh, J.: Towards realistic individual recourse and actionable explanations in black-box decision making systems. arXiv preprint arXiv:1907.09615 (2019)"},{"key":"25_CR9","unstructured":"Kingma, D.P.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"11","key":"25_CR10","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"2002","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (2002)","journal-title":"Proc. IEEE"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"25_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1007\/978-3-030-86520-7_40","volume-title":"Machine Learning and Knowledge Discovery in Databases. Research Track","author":"A Van Looveren","year":"2021","unstructured":"Van Looveren, A., Klaise, J.: Interpretable counterfactual explanations guided by prototypes. In: Oliver, N., P\u00e9rez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12976, pp. 650\u2013665. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86520-7_40"},{"key":"25_CR13","unstructured":"Melistas, T., et al.: Benchmarking counterfactual image generation. In: Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, 10\u201315 December 2024 (2024)"},{"key":"25_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/FRAI.2022.825565","volume":"5","author":"S Mertes","year":"2022","unstructured":"Mertes, S., Huber, T., Weitz, K., Heimerl, A., Andr\u00e9, E.: Ganterfactual - counterfactual explanations for medical non-experts using generative adversarial learning. Frontiers Artif. Intell. 5, 825565 (2022). https:\/\/doi.org\/10.3389\/FRAI.2022.825565","journal-title":"Frontiers Artif. Intell."},{"key":"25_CR15","unstructured":"Papamakarios, G., Murray, I., Pavlakou, T.: Masked autoregressive flow for density estimation. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017, Long Beach, CA, USA, pp. 2338\u20132347 (2017)"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Pawelczyk, M., Broelemann, K., Kasneci, G.: Learning model-agnostic counterfactual explanations for tabular data. In: Proceedings of the Web Conference 2020, pp. 3126\u20133132 (2020)","DOI":"10.1145\/3366423.3380087"},{"key":"25_CR17","unstructured":"Rezende, D., Mohamed, S.: Variational inference with normalizing flows. In: International Conference on Machine Learning, pp. 1530\u20131538. PMLR (2015)"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Rodr\u00edguez, P., et al.: Beyond trivial counterfactual explanations with diverse valuable explanations. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10\u201317 October 2021, pp. 1036\u20131045. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00109","DOI":"10.1109\/ICCV48922.2021.00109"},{"issue":"1","key":"25_CR19","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1038\/s41467-022-29268-7","volume":"13","author":"N Sapoval","year":"2022","unstructured":"Sapoval, N., et al.: Current progress and open challenges for applying deep learning across the biosciences. Nat. Commun. 13(1), 1728 (2022)","journal-title":"Nat. Commun."},{"key":"25_CR20","unstructured":"Sauer, A., Geiger, A.: Counterfactual generative networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021)"},{"key":"25_CR21","unstructured":"Theobald, C., Pennerath, F., Conan-Guez, B., Couceiro, M., Napoli, A.: Clarity: an improved gradient method for producing quality visual counterfactual explanations. arXiv preprint arXiv:2211.15370 (2022)"},{"key":"25_CR22","unstructured":"Tsiourvas, A., Sun, W., Perakis, G.: Manifold-aligned counterfactual explanations for neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 3763\u20133771. PMLR (2024)"},{"key":"25_CR23","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)","journal-title":"Harv. JL Tech."},{"key":"25_CR24","unstructured":"Wielopolski, P., Furman, O., Stefanowski, J., Zieba, M.: Probabilistically plausible counterfactual explanations with normalizing flows. In: ECAI 2024 - 27th European Conference on Artificial Intelligence, 19\u201324 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024). Frontiers in Artificial Intelligence and Applications, vol.\u00a0392, pp. 954\u2013961. IOS Press (2024)"},{"key":"25_CR25","doi-asserted-by":"publisher","unstructured":"Wolczyk, M., et al.: Plugen: multi-label conditional generation from pre-trained models. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 8647\u20138656. AAAI Press (2022). https:\/\/doi.org\/10.1609\/AAAI.V36I8.20843","DOI":"10.1609\/AAAI.V36I8.20843"},{"key":"25_CR26","unstructured":"Yu, Y., Zhang, W., Deng, Y.: Frechet inception distance (FID) for evaluating GANs. China University of Mining Technology Beijing Graduate School, vol. 3, no. 11 (2021)"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Zhong, X., Gallagher, B., Liu, S., Kailkhura, B., Hiszpanski, A., Han, T.Y.J.: Explainable machine learning in materials science. NPJ Comput. Mater. 8(1), 204 (2022)","DOI":"10.1038\/s41524-022-00884-7"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29924-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:04:50Z","timestamp":1782497090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29924-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032299239","9783032299246"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29924-6_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"27 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hamburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2026\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}