{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T07:20:29Z","timestamp":1775546429454,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"DIGICOSME"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Math Imaging Vis"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s10851-022-01077-z","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T09:08:03Z","timestamp":1649840883000},"page":"569-585","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7415-7746","authenticated-orcid":false,"given":"Chi-Hieu","family":"Pham","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sa\u00efd","family":"Ladjal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alasdair","family":"Newson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"1077_CR1","unstructured":"Kingma, D.P.,Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)"},{"key":"1077_CR2","unstructured":"Van\u00a0den Aaron, O., Nal, K., Lasse, E., Oriol, V., Alex, G., et\u00a0al.: Conditional image generation with pixelcnn decoders. In: Advances in Neural Information Processing Systems, pp. 4790\u20134798 (2016)"},{"key":"1077_CR3","unstructured":"Casper,\u00a0K., S\u00f8nderby, T., Raiko, L., Maal\u00f8e, S.,\u00a0Kaae, S., Ole, W.: Ladder variational autoencoders. In: Advances in Neural Information Processing Systems, pp. 3738\u20133746 (2016)"},{"key":"1077_CR4","unstructured":"Ilya, T., Olivier, B., Sylvain, G., Bernhard, S.: Wasserstein auto-encoders. In: International Conference on Learning Representations (2018)"},{"key":"1077_CR5","unstructured":"Huaibo, H., Ran, H., Zhenan, S., Tieniu, T., et\u00a0al.: Introvae: introspective variational autoencoders for photographic image synthesis. In: Advances in Neural Information Processing Systems, pp. 52\u201363 (2018)"},{"key":"1077_CR6","unstructured":"Ari, H., Arno, S., Juho, K.: Towards photographic image manipulation with balanced growing of generative autoencoders. In The IEEE Winter Conference on Applications of Computer Vision, pp. 3120\u20133129 (2020)"},{"key":"1077_CR7","unstructured":"Ian, G., Jean, P.-A., Mehdi, M., Bing, X., David, W.-F., Sherjil, O., Aaron, C., Yoshua, B.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"1077_CR8","unstructured":"Tim, S., Ian, G., Wojciech, Zaremba, V., Cheung, A., Radford, X.,\u00a0Chen: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, pp. 2234\u20132242 (2016)"},{"key":"1077_CR9","unstructured":"Tero, Karras, Timo, Aila, Samuli, Laine, Jaakko, Lehtinen: Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations, 2018"},{"key":"1077_CR10","unstructured":"Akash, S., Lazar, V., Chris, R., Michael, U., Gutmann, C., Sutton: Veegan: reducing mode collapse in gans using implicit variational learning. In: Advances in Neural Information Processing Systems, pp. 3308\u20133318 (2017)"},{"key":"1077_CR11","unstructured":"Tero, K., Samuli, L., Timo, A.: A style-based generator architecture for generative adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2019)"},{"key":"1077_CR12","unstructured":"Yunjey, C., Minje, C., Munyoung, K., Jung-Woo, H., Sunghun, K., Jaegul, C.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018)"},{"key":"1077_CR13","unstructured":"Jun-Yan, Z., Richard, Z., Deepak, P., Trevor, D., Alexei, A., Efros, O., Wang, E., Shechtman: Toward multimodal image-to-image translation. In: Advances in Neural Information Processing Systems, pp. 465\u2013476 (2017)"},{"key":"1077_CR14","unstructured":"Chi-Hieu, P., Carlos, T., H\u00e9l\u00e8ne, M., Nathalie, B., Ronan, F., Nicolas, P., Fran\u00e7ois, R.: Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI. In: International Symposium on Biomedical Imaging, pp. 991\u2013994 (2019)"},{"key":"1077_CR15","unstructured":"Durk, P., Kingma, S., Mohamed, D.,\u00a0Jimenez, R., Max, W.: Semi-supervised learning with deep generative models. In: Advances in Neural Information Processing Systems, pp. 3581\u20133589 (2014)"},{"key":"1077_CR16","unstructured":"Scott, R., Kihyuk, S., Yuting, Z., Honglak, L.: Learning to disentangle factors of variation with manifold interaction. In: International Conference on Machine Learning, pp. 1431\u20131439 (2014)"},{"key":"1077_CR17","unstructured":"Michael, F., Mathieu, J.,\u00a0Jake, Z., Junbo, Z., Aditya, R., Pablo, S., Yann, L.: Disentangling factors of variation in deep representation using adversarial training. In: Advances in Neural Information Processing Systems, pp. 5040\u20135048 (2016)"},{"key":"1077_CR18","unstructured":"Emily, L., Denton, et\u00a0al.: Unsupervised learning of disentangled representations from video. In: Advances in Neural Information Processing Systems, pp. 4414\u20134423 (2017)"},{"key":"1077_CR19","unstructured":"Wei-Ning, H., Yu, Z., James, G.: Unsupervised learning of disentangled and interpretable representations from sequential data. In: Advances in Neural Information Processing Systems, pp. 1878\u20131889 (2017)"},{"key":"1077_CR20","unstructured":"Narayanaswamy, S., Brooks, P., Jan-Willem, Van\u00a0de, M., Alban, D., Noah, G., Pushmeet, K., Frank, Wood, Philip, Tood: Learning disentangled representations with semi-supervised deep generative models. In: Advances in Neural Information Processing Systems, pp. 5925\u20135935 (2017)"},{"key":"1077_CR21","unstructured":"Marc\u2019Aurelio, R., Y-Lan, B., Yann, L., Cun: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, pp. 1185\u20131192 (2008)"},{"key":"1077_CR22","unstructured":"Xavier, Glorot, A., Bordes, Y.: Bengio: deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (2011)"},{"key":"1077_CR23","unstructured":"Alireza, M., Brendan, F.: K-sparse autoencoders. In: International Conference on Learning Representations (2014)"},{"key":"1077_CR24","unstructured":"Alec, R., Luke, M., Soumith, C.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (2016)"},{"key":"1077_CR25","unstructured":"Martin, A., Soumith, C., L\u00e9on, B.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223 (2017)"},{"key":"1077_CR26","unstructured":"Ishaan, G., Faruk, A., Martin, A., Vincent, D., Aaron, C.: Courville: improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767\u20135777 (2017)"},{"key":"1077_CR27","unstructured":"Xi, C., Yan, D., Rein, H., John, S., Ilya, S., Pieter, A.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172\u20132180 (2016)"},{"key":"1077_CR28","unstructured":"Augustus, O., Christopher, O., Jonathon, S.: Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2642\u20132651. JMLR. org (2017)"},{"key":"1077_CR29","doi-asserted-by":"crossref","unstructured":"Xinchen, Y., Jimei, Y., Kihyuk, S., Honglak, L.: Attribute2image: conditional image generation from visual attributes. In: European Conference on Computer Vision, pp. 776\u2013791. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_47"},{"key":"1077_CR30","doi-asserted-by":"crossref","unstructured":"Sudipto, M., Himanshu, A., Eugene, L., Sreeram, K.: Clustergan: latent space clustering in generative adversarial networks. In: Proceedings of the AAAI Conference on Artificial Intelligence 33, pp. 4610\u20134617 (2019)","DOI":"10.1609\/aaai.v33i01.33014610"},{"key":"1077_CR31","doi-asserted-by":"crossref","unstructured":"Quentin, D., Chi-Hieu, P., Cl\u00e9ment, C., Carlos, T., Guillaume, D., H\u00e9l\u00e8ne, M., Nathalie, B., Ronan, F., Nicolas, P., Fran\u00e7ois, R.: SegSRGAN: super-resolution and segmentation uing generative adversarial networks $$-$$ application to neonatal brain MRI. Comput. Biol. Med., 103755 (2020)","DOI":"10.1016\/j.compbiomed.2020.103755"},{"key":"1077_CR32","doi-asserted-by":"crossref","unstructured":"Salah, R., Yoshua, B., Aaron, C., Pascal, V., Mehdi, M.: Disentangling factors of variation for facial expression recognition. In: European Conference on Computer Vision, pp. 808\u2013822. Springer (2012)","DOI":"10.1007\/978-3-642-33783-3_58"},{"key":"1077_CR33","unstructured":"Brian, C., Jesse, A., Livezey, A.K., Bansal, B.A.: Olshausen: discovering hidden factors of variation in deep networks. arXiv:1412.6583 (2014)"},{"key":"1077_CR34","unstructured":"Abhishek, K., Prasanna, S., Avinash, B.: Variational inference of disentangled latent concepts from unlabeled observations. In: International Conference on Learning Representations (2018)"},{"key":"1077_CR35","unstructured":"Jos\u00e9, L.: Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision. In: International Conference on Learning Representations (2019)"},{"key":"1077_CR36","unstructured":"Guillaume, L., Neil, Z., Nicolas, U., Antoine, B., Ludovic, D., et\u00a0al.: Fader networks: manipulating images by sliding attributes. In: Advances in Neural Information Processing Systems, pp. 5967\u20135976 (2017)"},{"key":"1077_CR37","unstructured":"Irina, H., Loic, M., Arka, P., Christopher, B., Xavier, G., Matthew, B., Shakir, M., Alexander, L.: $$\\beta $$-vae: learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Representations, vol.\u00a02, p.\u00a06 (2017)"},{"key":"1077_CR38","unstructured":"Christopher, P., Burgess, I., Higgins, A., Pal, L., Matthey, N., Watters, G., Desjardins, A., Lerchner.: Understanding disentangling in $$\\beta $$-vae. In: NIPS Workshop on Learning Disentangled Representations (2018)"},{"key":"1077_CR39","unstructured":"Hyunjik, K., Andriy, M.: Disentangling by factorising. In: International Conference on Machine Learning (2018)"},{"key":"1077_CR40","unstructured":"Tian,\u00a0Q., Chen, X., Li, R.B, Grosse, D.K., Duvenaud.: Isolating sources of disentanglement in variational autoencoders. In: Advances in Neural Information Processing Systems, pp. 2610\u20132620 (2018)"},{"key":"1077_CR41","doi-asserted-by":"crossref","unstructured":"Wei, W., Dan, Y., Feiyu, C., Yunsheng, P., Sheng, H., Yongxin, G.: Clustering with orthogonal autoencoder. IEEE. Access 7, 62421\u201362432 (2019)","DOI":"10.1109\/ACCESS.2019.2916030"},{"key":"1077_CR42","unstructured":"Yaodong, Y., Kwan, H.R., Chan, C., You, C., Song, Y.M.: Learning diverse and discriminative representations via the principle of maximal coding rate reduction. Advances in Neural Information Processing Systems, 33 (2020)"},{"key":"1077_CR43","unstructured":"Sergey, I., Christian, S.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 (2015)"},{"key":"1077_CR44","unstructured":"Diederik, K., Jimmy, B.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"1077_CR45","unstructured":"Ziwei, L., Ping, L., Xiaogang, W., Xiaoou, T.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (2015)"},{"key":"1077_CR46","unstructured":":\/\/www.faceplusplus.com\/.Face++ cognitive services"}],"container-title":["Journal of Mathematical Imaging and Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10851-022-01077-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10851-022-01077-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10851-022-01077-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T09:38:42Z","timestamp":1653557922000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10851-022-01077-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,13]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["1077"],"URL":"https:\/\/doi.org\/10.1007\/s10851-022-01077-z","relation":{},"ISSN":["0924-9907","1573-7683"],"issn-type":[{"value":"0924-9907","type":"print"},{"value":"1573-7683","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,13]]},"assertion":[{"value":"14 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}