{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T16:46:21Z","timestamp":1765039581375,"version":"3.28.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,19]]},"DOI":"10.1109\/icip42928.2021.9506780","type":"proceedings-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T21:08:41Z","timestamp":1629752921000},"page":"2513-2517","source":"Crossref","is-referenced-by-count":2,"title":["Hierarchical Variational Autoencoders For Visual Counterfactuals"],"prefix":"10.1109","author":[{"given":"Nicolas","family":"Vercheval","sequence":"first","affiliation":[{"name":"Ghent University,TELIN-GAIM, Faculty of Engineering and Architecture,Department of Telecommunications and Information Processing,Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandra","family":"Pizurica","sequence":"additional","affiliation":[{"name":"Ghent University,TELIN-GAIM, Faculty of Engineering and Architecture,Department of Telecommunications and Information Processing,Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","article-title":"NVAE: A Deep Hierarchical Variational Autoencoder","author":"vahdat","year":"2020","journal-title":"NeurIPS"},{"key":"ref32","article-title":"Axiomatic attribution for deep networks","author":"sundararajan","year":"2017","journal-title":"In ICML17"},{"key":"ref31","article-title":"Ladder Variational Autoencoders","author":"s\u00f8nderby","year":"2016","journal-title":"Advances in neural information processing systems"},{"year":"2021","author":"seitzer","journal-title":"pytorch-fid FID Score for PyTorch Version 0 1 1","key":"ref30"},{"year":"2020","key":"ref34"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/CVPR.2019.00453"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1007\/978-3-030-58342-2_11"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1561\/9781680836233"},{"key":"ref13","article-title":"Squeezeand-Excitation Networks","author":"hu","year":"2017","journal-title":"arXiv preprint arXiv 1709 01922"},{"key":"ref14","article-title":"Impartial predictive modeling: Ensuring fairness in arbitrary models","author":"johnson","year":"2017","journal-title":"NIPS"},{"year":"0","author":"lara","article-title":"Inference from Explanation","key":"ref15"},{"key":"ref16","article-title":"Deep learning face attributes inthe wild","author":"liu","year":"2015","journal-title":"ICCV"},{"key":"ref17","article-title":"Causal Effect Inference with Deep Latent-Variable Models","author":"louizos","year":"2017","journal-title":"NIPS"},{"key":"ref18","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"lundberg","year":"2017","journal-title":"In Advances in Neural Information Processing Systems"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1002\/adma.201901111"},{"key":"ref4","article-title":"Counterfactuals uncover the modular structure of deep generative models","author":"besserve","year":"2020","journal-title":"ICLRE"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.4018\/IJMDEM.2018100101"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.24963\/ijcai.2019\/876"},{"key":"ref27","article-title":"A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI","author":"tjoa","year":"2019","journal-title":"Pre-print"},{"key":"ref6","article-title":"Variational Lossy Autoencoder","author":"chen","year":"2017","journal-title":"ICLRE"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/IJCNN48605.2020.9206728"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref8","article-title":"?-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework","author":"higgins","year":"2017","journal-title":"ICLRE"},{"key":"ref7","article-title":"AttGAN: Facial Attribute Editing by Only Changing What You Want","author":"he","year":"2018","journal-title":"In IEEE Transactions on Image Processing"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/ACCESS.2020.3034828"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.18653\/v1\/D17-1042"},{"key":"ref9","article-title":"Arbitrary style transfer in realtime with adaptive instance normalization","author":"huang","year":"2017","journal-title":"CoRR"},{"key":"ref20","article-title":"BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling","author":"mal\u00f8e","year":"2019","journal-title":"NIPS"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1017\/CBO9780511803161"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1145\/3366423.3380087"},{"key":"ref24","article-title":"Learning Structured Output Representation using Deep Conditional Generative Models","author":"sohn","year":"2015","journal-title":"NIPS"},{"key":"ref23","article-title":"Counterfactual Reasoning for Fair Clinical Risk Prediction","author":"pfohl","year":"2019","journal-title":"Machine Learning for Healthcare Conference"},{"key":"ref26","article-title":"Searching for Activation Functions","author":"ramachandran","year":"2018","journal-title":"ICLRE"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1145\/2939672.2939778"}],"event":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","start":{"date-parts":[[2021,9,19]]},"location":"Anchorage, AK, USA","end":{"date-parts":[[2021,9,22]]}},"container-title":["2021 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9506008\/9506009\/09506780.pdf?arnumber=9506780","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:05:42Z","timestamp":1670371542000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9506780\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,19]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/icip42928.2021.9506780","relation":{},"subject":[],"published":{"date-parts":[[2021,9,19]]}}}