{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T07:42:48Z","timestamp":1782286968340,"version":"3.54.5"},"reference-count":93,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T00:00:00Z","timestamp":1621036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T00:00:00Z","timestamp":1621036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be <jats:italic>accurate<\/jats:italic> by standard evaluation metrics and benchmarks, a surge of work has recently exposed the demographic bias that such algorithms exhibit\u2013highlighting that <jats:italic>accuracy<\/jats:italic> does not entail <jats:italic>fairness<\/jats:italic>. Clearly, deploying biased systems under real-world settings can have grave consequences for affected populations. Indeed, learning methods are prone to inheriting, or even amplifying the bias present in a training set, manifested by uneven representation across demographic groups. In facial datasets, this particularly relates to attributes such as <jats:italic>skin tone<\/jats:italic>, <jats:italic>gender<\/jats:italic>, and <jats:italic>age<\/jats:italic>. In this work, we address the problem of mitigating bias in facial datasets by data augmentation. We propose a multi-attribute framework that can successfully transfer complex, multi-scale facial patterns <jats:italic>even<\/jats:italic> if these belong to underrepresented groups in the training set. This is achieved by relaxing the rigid dependence on a single attribute label, and further introducing a tensor-based mixing structure that captures multiplicative interactions between attributes in a multilinear fashion. We evaluate our method with an extensive set of qualitative and quantitative experiments on several datasets, with rigorous comparisons to state-of-the-art methods. We find that the proposed framework can successfully mitigate dataset bias, as evinced by extensive evaluations on established <jats:italic>diversity<\/jats:italic> metrics, while significantly improving fairness metrics such as equality of opportunity.<\/jats:p>","DOI":"10.1007\/s11263-021-01448-w","type":"journal-article","created":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T08:03:06Z","timestamp":1621065786000},"page":"2288-2307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer"],"prefix":"10.1007","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5928-515X","authenticated-orcid":false,"given":"Markos","family":"Georgopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Oldfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihalis A.","family":"Nicolaou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannis","family":"Panagakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maja","family":"Pantic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,15]]},"reference":[{"key":"1448_CR1","doi-asserted-by":"crossref","unstructured":"Alvi, M., Zisserman, A., & Nell\u00e5ker, C. (2018). Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings. In Proceedings of the European conference on computer vision (ECCV) (p. 0)","DOI":"10.1007\/978-3-030-11009-3_34"},{"key":"1448_CR2","unstructured":"Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein gan"},{"key":"1448_CR3","unstructured":"Arora, S., Zhang, Y. (2017). Do GANs actually learn the distribution? An empirical study. arXiv preprint arXiv:1706.08224"},{"issue":"1","key":"1448_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1177\/0146167289151002","volume":"15","author":"RK Bothwell","year":"1989","unstructured":"Bothwell, R. K., Brigham, J. C., & Malpass, R. S. (1989). Cross-racial identification. Personality and Social Psychology Bulletin, 15(1), 19\u201325.","journal-title":"Personality and Social Psychology Bulletin"},{"key":"1448_CR5","unstructured":"Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis."},{"key":"1448_CR6","unstructured":"Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77\u201391)."},{"issue":"3","key":"1448_CR7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/BF02310791","volume":"35","author":"JD Carroll","year":"1970","unstructured":"Carroll, J. D., & Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart\u2013Young\u00c2 decomposition. Psychometrika, 35(3), 283\u2013319.","journal-title":"Psychometrika"},{"key":"1448_CR8","doi-asserted-by":"crossref","unstructured":"Chen, B.C., Chen, C.S., & Hsu, W.H. (2014). Cross-age reference coding for age-invariant face recognition and retrieval. In Proceedings of the European conference on computer vision (ECCV).","DOI":"10.1007\/978-3-319-10599-4_49"},{"key":"1448_CR9","doi-asserted-by":"publisher","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., & Choo, J. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In 2018 IEEE\/CVF conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/cvpr.2018.00916.","DOI":"10.1109\/cvpr.2018.00916"},{"issue":"3","key":"1448_CR10","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/TIFS.2015.2480381","volume":"11","author":"A Dantcheva","year":"2015","unstructured":"Dantcheva, A., Elia, P., & Ross, A. (2015). What else does your biometric data reveal? A survey on soft biometrics. IEEE Transactions on Information Forensics and Security, 11(3), 441\u2013467.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"1448_CR11","unstructured":"Dua, D., & Graff, C. (2017). UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml."},{"key":"1448_CR12","doi-asserted-by":"crossref","unstructured":"Duong, C.N., Luu, K., Quach, K.G., Nguyen, N., Patterson, E., Bui, T.D., & Le, N. (2019). Automatic face aging in videos via deep reinforcement learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 10013\u201310022).","DOI":"10.1109\/CVPR.2019.01025"},{"key":"1448_CR13","unstructured":"Edwards, H., & Storkey, A. (2015). Censoring representations with an adversary. arXiv preprint arXiv:1511.05897."},{"issue":"11","key":"1448_CR14","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TPAMI.2010.36","volume":"32","author":"Y Fu","year":"2010","unstructured":"Fu, Y., Guo, G., & Huang, T. S. (2010). Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 1955\u20131976.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1448_CR15","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv:1508.06576.","DOI":"10.1167\/16.12.326"},{"key":"1448_CR16","unstructured":"Georgopoulos, M., Chrysos, G., Pantic, M., & Panagakis, Y. (2020). Multilinear latent conditioning for generating unseen attribute combinations. In International conference on machine learning"},{"key":"1448_CR17","doi-asserted-by":"publisher","unstructured":"Georgopoulos, M., Oldfield, J., Nicolaou, M.A., Panagakis, Y., & Pantic, M. (2020). Enhancing facial data diversity with style-based face aging. In 2020 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), IEEE (pp. 66\u201374), Seattle, WA, USA. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00015. https:\/\/ieeexplore.ieee.org\/document\/9150573\/.","DOI":"10.1109\/CVPRW50498.2020.00015"},{"key":"1448_CR18","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.imavis.2018.05.003","volume":"80","author":"M Georgopoulos","year":"2018","unstructured":"Georgopoulos, M., Panagakis, Y., & Pantic, M. (2018). Modeling of facial aging and kinship: A survey. Image and Vision Computing, 80, 58\u201379.","journal-title":"Image and Vision Computing"},{"key":"1448_CR19","doi-asserted-by":"publisher","unstructured":"Georgopoulos, M., Panagakis, Y., & Pantic, M. (2020). Investigating bias in deep face analysis: The kanface dataset and empirical study. Image and Vision Computing. https:\/\/doi.org\/10.1016\/j.imavis.2020.103954.","DOI":"10.1016\/j.imavis.2020.103954"},{"key":"1448_CR20","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks"},{"key":"1448_CR21","unstructured":"Grover, A., Choi, K., Shu, R., & Ermon, S. (2019a). Fair generative modeling via weak supervision. arXiv preprint arXiv:1910.12008."},{"key":"1448_CR22","unstructured":"Grover, A., Song, J., Kapoor, A., Tran, K., Agarwal, A., Horvitz, E.J., & Ermon, S. (2019b). Bias correction of learned generative models using likelihood-free importance weighting. In Advances in neural information processing systems (pp. 11058\u201311070)."},{"key":"1448_CR23","unstructured":"Hardt, M., Price, E., Srebro, N., & et\u00a0al. (2016). Equality of opportunity in supervised learning. In Advances in neural information processing systems (pp. 3315\u20133323)."},{"key":"1448_CR24","unstructured":"Harshman, R. A., et\u00a0al. (1970). Foundations of the parafac procedure: Models and conditions for an \u201c explanatory\u201d multimodal factor analysis."},{"key":"1448_CR25","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr.2016.90.","DOI":"10.1109\/cvpr.2016.90"},{"key":"1448_CR26","unstructured":"He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X. (2017). Arbitrary facial attribute editing: Only change what you want. arXiv:1711.10678."},{"issue":"11","key":"1448_CR27","doi-asserted-by":"publisher","first-page":"54645478","DOI":"10.1109\/TIP.2019.2916751","volume":"28","author":"Z He","year":"2019","unstructured":"He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X. (2019). Attgan: Facial attribute editing by only changing what you want. IEEE Transactions on Image Processing, 28(11), 54645478.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1448_CR28","doi-asserted-by":"crossref","unstructured":"Hendricks, L.A., Burns, K., Saenko, K., Darrell, T., & Rohrbach, A. (2018). Women also snowboard: Overcoming bias in captioning models. In European conference on computer vision (pp. 793\u2013811). Springer.","DOI":"10.1007\/978-3-030-01219-9_47"},{"key":"1448_CR29","doi-asserted-by":"crossref","unstructured":"Holstein, K., Wortman\u00a0Vaughan, J., Daum\u00e9\u00a0III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? In: Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1\u201316).","DOI":"10.1145\/3290605.3300830"},{"key":"1448_CR30","unstructured":"Huang, G.B., Mattar, M., Berg, T., & Learned-Miller, E. (2008). Labeled faces in the wild: A database forstudying face recognition in unconstrained environments."},{"key":"1448_CR31","doi-asserted-by":"crossref","unstructured":"Huang, X., & Belongie, S. (2017). Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. arXiv:1703.06868.","DOI":"10.1109\/ICCV.2017.167"},{"key":"1448_CR32","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M.Y., Belongie, S., & Kautz, J. (2018). Multimodal unsupervised image-to-image translation. arXiv:1804.04732.","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"1448_CR33","unstructured":"Inoue, H. (2018). Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929"},{"key":"1448_CR34","doi-asserted-by":"publisher","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., & Efros, A.A. (2017). Image-to-image translation with conditional adversarial networks. In 2017 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr.2017.632.","DOI":"10.1109\/cvpr.2017.632"},{"key":"1448_CR35","unstructured":"Jackson, P. T., Abarghouei, A. A., Bonner, S., Breckon, T. P., & Obara, B. (2019). Style augmentation: data augmentation via style randomization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops."},{"key":"1448_CR36","unstructured":"Jayakumar, S.M., Menick, J., Czarnecki, W.M., Schwarz, J., Rae, J., Osindero, S., Teh, Y.W., Harley, T., & Pascanu, R. (2020). Multiplicative interactions and where to find them. In International conference on learning representations."},{"key":"1448_CR37","unstructured":"Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of GANs for improved quality, stability, and variation."},{"key":"1448_CR38","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. arXiv:1812.04948.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"1448_CR39","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, H., Kim, K., Kim, S., & Kim, J. (2019). Learning not to learn: Training deep neural networks with biased data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9012\u20139020).","DOI":"10.1109\/CVPR.2019.00922"},{"key":"1448_CR40","unstructured":"Kim, J., Kim, M., Kang, H., & Lee, K. (2020). U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. arXiv:1907.10830."},{"key":"1448_CR41","unstructured":"Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization. International Conference on Learning Representations."},{"key":"1448_CR42","doi-asserted-by":"crossref","unstructured":"Kolda, T. G. (2006). Multilinear operators for higher-order decompositions. Technical Reports, Sandia National Laboratories.","DOI":"10.2172\/923081"},{"issue":"3","key":"1448_CR43","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM Review, 51(3), 455\u2013500.","journal-title":"SIAM Review"},{"key":"1448_CR44","doi-asserted-by":"crossref","unstructured":"Kuhlman, C., Jackson, L., & Chunara, R. (2020). No computation without representation: Avoiding data and algorithm biases through diversity. arXiv preprint arXiv:2002.11836.","DOI":"10.1145\/3394486.3411074"},{"key":"1448_CR45","unstructured":"Lanitis, A. (2002). FG-NET Aging Database."},{"key":"1448_CR46","unstructured":"Li, M., Zuo, W., & Zhang, D. (2016). Deep identity-aware transfer of facial attributes."},{"key":"1448_CR47","doi-asserted-by":"crossref","unstructured":"Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing.","DOI":"10.1109\/TAFFC.2020.2981446"},{"key":"1448_CR48","unstructured":"Lim, J.H., & Ye, J.C. (2017). Geometric GAN."},{"key":"1448_CR49","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., & Kautz, J. (2019). Few-shot unsupervised image-to-image translation. arXiv: 1905.01723v2.","DOI":"10.1109\/ICCV.2019.01065"},{"key":"1448_CR50","unstructured":"Liu, Y., Li, Q., Sun, Z., & Tan, T. (2019). A3gan: An attribute-aware attentive generative adversarial network for face aging."},{"key":"1448_CR51","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2015.425"},{"key":"1448_CR52","unstructured":"Ma, L., Jia, X., Georgoulis, S., Tuytelaars, T., & Gool, L.V. (2018). Exemplar guided unsupervised image-to-image translation with semantic consistency."},{"key":"1448_CR53","unstructured":"Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2018). Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309."},{"key":"1448_CR54","doi-asserted-by":"publisher","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., & Smolley, S.P. (2017). Least squares generative adversarial networks. In 2017 IEEE international conference on computer vision (ICCV). https:\/\/doi.org\/10.1109\/iccv.2017.304.","DOI":"10.1109\/iccv.2017.304"},{"key":"1448_CR55","doi-asserted-by":"crossref","unstructured":"Masi, I., Wu, Y., Hassner, T., & Natarajan, P. (2018) Deep face recognition: A survey. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), IEEE (pp. 471\u2013478).","DOI":"10.1109\/SIBGRAPI.2018.00067"},{"key":"1448_CR56","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635."},{"key":"1448_CR57","unstructured":"Merler, M., Ratha, N., Feris, R. S., & Smith, J. R. (2019). Diversity in faces."},{"key":"1448_CR58","unstructured":"Mescheder, L., Geiger, A., & Nowozin, S. (2018). Which Training Methods for GANs do actually Converge?arXiv:1801.04406 [cs]."},{"key":"1448_CR59","unstructured":"Nagpal, S., Singh, M., Singh, R., Vatsa, M., & Ratha, N. (2019). Deep learning for face recognition: Pride or prejudiced? arXiv preprint arXiv:1904.01219."},{"key":"1448_CR60","unstructured":"Ng, C.B., Tay, Y.H., & Goi, B.M. (2012). Vision-based human gender recognition: A survey. arXiv preprint arXiv:1204.1611."},{"key":"1448_CR61","unstructured":"Odena, A., Olah, C., & Shlens, J. (2017). Conditional image synthesis with auxiliary classifier GANs. In Proceedings of the 34th international conference on machine learning (Vol. 70, pp. 2642\u20132651). JMLR. org."},{"key":"1448_CR62","doi-asserted-by":"publisher","unstructured":"Park, T., Liu, M.Y., Wang, T.C., & Zhu, J.Y. (2019). Semantic image synthesis with spatially-adaptive normalization. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr.2019.00244.","DOI":"10.1109\/cvpr.2019.00244"},{"key":"1448_CR63","unstructured":"Perarnau, G., van de Weijer, J., Raducanu, B., & Ivarez, J.M. (2016). Invertible conditional GANs for image editing."},{"key":"1448_CR64","unstructured":"Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621."},{"key":"1448_CR65","doi-asserted-by":"crossref","unstructured":"Quadrianto, N., Sharmanska, V., & Thomas, O. (2018). Discovering fair representations in the data domain.","DOI":"10.1109\/CVPR.2019.00842"},{"key":"1448_CR66","doi-asserted-by":"crossref","unstructured":"Quadrianto, N., Sharmanska, V., & Thomas, O. (2019). Discovering fair representations in the data domain. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8227\u20138236).","DOI":"10.1109\/CVPR.2019.00842"},{"key":"1448_CR67","unstructured":"Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks."},{"key":"1448_CR68","doi-asserted-by":"crossref","unstructured":"Raji, I.D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. In Proceedings of the 2019 AAAI\/ACM conference on AI, ethics, and society (pp. 429\u2013435).","DOI":"10.1145\/3306618.3314244"},{"key":"1448_CR69","unstructured":"Ramanathan, N., Chellappa, R., Biswas, S., et\u00a0al. (2009). Age progression in human faces: A survey. Visual Languages and Computing, 15, 3349\u20133361."},{"key":"1448_CR70","doi-asserted-by":"publisher","unstructured":"Ricanek, K., & Tesafaye, T. (2006). Morph: A longitudinal image database of normal adult age-progression. In 7th international conference on automatic face and gesture recognition (FGR06) (pp. 341\u2013345). https:\/\/doi.org\/10.1109\/FGR.2006.78.","DOI":"10.1109\/FGR.2006.78"},{"issue":"2\u20134","key":"1448_CR71","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11263-016-0940-3","volume":"126","author":"R Rothe","year":"2018","unstructured":"Rothe, R., Timofte, R., & Gool, L. V. (2018). Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision, 126(2\u20134), 144\u2013157.","journal-title":"International Journal of Computer Vision"},{"key":"1448_CR72","unstructured":"Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training GANs. In CoRR. arXiv:1606.03498."},{"issue":"1","key":"1448_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-52737-x","volume":"9","author":"V Sandfort","year":"2019","unstructured":"Sandfort, V., Yan, K., Pickhardt, P. J., & Summers, R. M. (2019). Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in CT segmentation tasks. Scientific Reports, 9(1), 1\u20139.","journal-title":"Scientific Reports"},{"key":"1448_CR74","unstructured":"Sattigeri, P., Hoffman, S.C., Chenthamarakshan, V., & Varshney, K.R. (2018). Fairness GAN."},{"key":"1448_CR75","doi-asserted-by":"publisher","first-page":"264","DOI":"10.3389\/fnagi.2016.00264","volume":"8","author":"A Schaich","year":"2016","unstructured":"Schaich, A., Obermeyer, S., Kolling, T., & Knopf, M. (2016). An own-age bias in recognizing faces with horizontal information. Frontiers in Aging Neuroscience, 8, 264.","journal-title":"Frontiers in Aging Neuroscience"},{"key":"1448_CR76","unstructured":"Serna, I., Morales, A., Fierrez, J., Cebrian, M., Obradovich, N., & Rahwan, I. (2019). Algorithmic discrimination: Formulation and exploration in deep learning-based face biometrics. arXiv preprint arXiv:1912.01842."},{"issue":"1","key":"1448_CR77","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 60.","journal-title":"Journal of Big Data"},{"key":"1448_CR78","unstructured":"Tang, H., Liu, H., Xu, D., Torr, P.H.S., & Sebe, N. (2019). Attentiongan: Unpaired image-to-image translation using attention-guided generative adversarial networks."},{"key":"1448_CR79","doi-asserted-by":"crossref","unstructured":"Verma, S., & Rubin, J. (2018). Fairness definitions explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare), IEEE (pp. 1\u20137).","DOI":"10.1145\/3194770.3194776"},{"key":"1448_CR80","doi-asserted-by":"crossref","unstructured":"Wang, M., Deng, W., Hu, J., Tao, X., & Huang, Y. (2019). Racial faces in the wild: Reducing racial bias by information maximization adaptation network. In Proceedings of the IEEE international conference on computer vision, pp. 692\u2013702.","DOI":"10.1109\/ICCV.2019.00078"},{"key":"1448_CR81","doi-asserted-by":"crossref","unstructured":"Wang, W., Cui, Z., Yan, Y., Feng, J., Yan, S., Shu, X., & Sebe, N. (2016). Recurrent face aging. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2378\u20132386).","DOI":"10.1109\/CVPR.2016.261"},{"key":"1448_CR82","doi-asserted-by":"crossref","unstructured":"Wang, Z., Qinami, K., Karakozis, I.C., Genova, K., Nair, P., Hata, K., & Russakovsky, O. (2020). Towards fairness in visual recognition: Effective strategies for bias mitigation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 8919\u20138928).","DOI":"10.1109\/CVPR42600.2020.00894"},{"key":"1448_CR83","doi-asserted-by":"crossref","unstructured":"Wang, Z. X., Tang, W. L., & Gao, S. (2018). Face aging with identity-preserved conditional generative adversarial networks. In 2018 IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00828"},{"key":"1448_CR84","doi-asserted-by":"publisher","unstructured":"Yang, H., Huang, D., Wang, Y., & Jain, A. K. (2018). Learning face age progression: A pyramid architecture of GANs. In 2018 IEEE\/CVF conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/cvpr.2018.00011.","DOI":"10.1109\/cvpr.2018.00011"},{"issue":"6","key":"1448_CR85","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TIP.2016.2547587","volume":"25","author":"H Yang","year":"2016","unstructured":"Yang, H., Huang, D., Wang, Y., Wang, H., & Tang, Y. (2016). Face aging effect simulation using hidden factor analysis joint sparse representation. IEEE Transactions on Image Processing, 25(6), 2493\u20132507.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1448_CR86","doi-asserted-by":"crossref","unstructured":"Yucer, S., Ak\u00e7ay, S., Al-Moubayed, N., & Breckon, T. P. (2020). Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation. arXiv preprint arXiv:2004.08945.","DOI":"10.1109\/CVPRW50498.2020.00017"},{"key":"1448_CR87","doi-asserted-by":"crossref","unstructured":"Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society (pp. 335\u2013340).","DOI":"10.1145\/3278721.3278779"},{"key":"1448_CR88","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., & Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412."},{"key":"1448_CR89","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., & Qi, H. (2017). Age progression\/regression by conditional adversarial autoencoder. In IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2017.463"},{"key":"1448_CR90","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K. W. (2017). Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457.","DOI":"10.18653\/v1\/D17-1323"},{"key":"1448_CR91","unstructured":"Zhao, S., Ren, H., Yuan, A., Song, J., Goodman, N., & Ermon, S. (2018). Bias and generalization in deep generative models: An empirical study. In Advances in Neural Information Processing Systems (pp. 10792\u201310801)."},{"key":"1448_CR92","doi-asserted-by":"crossref","unstructured":"Zheng, X., Chalasani, T., Ghosal, K., Lutz, S., & Smolic, A. (2019). Stada: Style transfer as data augmentation. arXiv preprint arXiv:1909.01056.","DOI":"10.5220\/0007353401070114"},{"key":"1448_CR93","doi-asserted-by":"publisher","unstructured":"Zhu, J.Y., Park, T., Isola, P., & Efros, A.A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE international conference on computer vision (ICCV). https:\/\/doi.org\/10.1109\/iccv.2017.244.","DOI":"10.1109\/iccv.2017.244"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-021-01448-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-021-01448-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-021-01448-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T07:31:57Z","timestamp":1623223917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-021-01448-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,15]]},"references-count":93,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["1448"],"URL":"https:\/\/doi.org\/10.1007\/s11263-021-01448-w","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,15]]},"assertion":[{"value":"15 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}