{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:36:51Z","timestamp":1772642211627,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031736353","type":"print"},{"value":"9783031736360","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73636-0_14","type":"book-chapter","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T15:06:03Z","timestamp":1730732763000},"page":"230-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["From Fake to\u00a0Real: Pretraining on\u00a0Balanced Synthetic Images to\u00a0Prevent Spurious Correlations in\u00a0Image Recognition"],"prefix":"10.1007","author":[{"given":"Maan","family":"Qraitem","sequence":"first","affiliation":[]},{"given":"Kate","family":"Saenko","sequence":"additional","affiliation":[]},{"given":"Bryan A.","family":"Plummer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"14_CR1","unstructured":"Ahmed, F., Bengio, Y., Van\u00a0Seijen, H., Courville, A.: Systematic generalisation with group invariant predictions. In: International Conference on Learning Representations (2020)"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Bianchi, F., et al.: Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 1493\u20131504 (2023)","DOI":"10.1145\/3593013.3594095"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Corvi, R., Cozzolino, D., Zingarini, G., Poggi, G., Nagano, K., Verdoliva, L.: On the detection of synthetic images generated by diffusion models. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135 (2023). https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10095167","DOI":"10.1109\/ICASSP49357.2023.10095167"},{"key":"14_CR4","unstructured":"Gokhale, T., et al.: Benchmarking spatial relationships in text-to-image generation. arXiv preprint arXiv:2212.10015 (2022)"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Hirota, Y., Nakashima, Y., Garcia, N.: Gender and racial bias in visual question answering datasets. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 1280\u20131292 (2022)","DOI":"10.1145\/3531146.3533184"},{"key":"14_CR7","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. ArXiv abs\/2006.11239 (2020)"},{"key":"14_CR8","unstructured":"Hong, Y., Yang, E.: Unbiased classification through bias-contrastive and bias-balanced learning. In: Thirty-Fifth Conference on Neural Information Processing Systems (2021). https:\/\/openreview.net\/forum?id=2OqZZAqxnn"},{"key":"14_CR9","unstructured":"Joshi, S., Yang, Y., Xue, Y., Yang, W., Mirzasoleiman, B.: Towards mitigating spurious correlations in the wild: a benchmark & a more realistic dataset. arXiv preprint arXiv:2306.11957 (2023)"},{"key":"14_CR10","doi-asserted-by":"publisher","unstructured":"Kim, B., Kim, H., Kim, K., Kim, S., Kim, J.: Learning not to learn: training deep neural networks with biased data. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9004\u20139012 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00922","DOI":"10.1109\/CVPR.2019.00922"},{"key":"14_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)"},{"key":"14_CR12","unstructured":"Kirichenko, P., Izmailov, P., Wilson, A.G.: Last layer re-training is sufficient for robustness to spurious correlations. In: ICLR (2023)"},{"issue":"7","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1007\/s11263-020-01316-z","volume":"128","author":"A Kuznetsova","year":"2020","unstructured":"Kuznetsova, A., et al.: The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale. Int. J. Comput. Vis. 128(7), 1956\u20131981 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Liu, Z., Wu, L., Luo, P.: Maskgan: towards diverse and interactive facial image manipulation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: A Whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20071\u201320082 (2023)","DOI":"10.1109\/CVPR52729.2023.01922"},{"key":"14_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014 Part V. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"14_CR17","unstructured":"Liu, Q., et al.: Discovering failure modes of text-guided diffusion models via adversarial search. In: ICLR (2023)"},{"key":"14_CR18","unstructured":"Liu, E.Z., et al.: Just train twice: improving group robustness without training group information. In: International Conference on Machine Learning, pp. 6781\u20136792. PMLR (2021)"},{"key":"14_CR19","unstructured":"Luccioni, A.S., Akiki, C., Mitchell, M., Jernite, Y.: Stable bias: analyzing societal representations in diffusion models. arXiv preprint arXiv:2303.11408 (2023)"},{"key":"14_CR20","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"14_CR21","unstructured":"Marcus, G., Davis, E., Aaronson, S.: A very preliminary analysis of DALL-E 2. arXiv preprint arXiv:2204.13807 (2022)"},{"key":"14_CR22","unstructured":"Meister, N., Zhao, D., Wang, A., Ramaswamy, V.V., Fong, R.C., Russakovsky, O.: Gender artifacts in visual datasets. ArXiv abs\/2206.09191 (2022)"},{"key":"14_CR23","unstructured":"Mondal, A.K., Singhal, L., Tiwary, P., Singla, P., Prathosh, A.P.: Minority oversampling for imbalanced data via class-preserving regularized auto-encoders. In: International Conference on Artificial Intelligence and Statistics (2023)"},{"key":"14_CR24","unstructured":"Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Qraitem, M., Saenko, K., Plummer, B.A.: Bias mimicking: a simple sampling approach for bias mitigation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20311\u201320320 (2023)","DOI":"10.1109\/CVPR52729.2023.01945"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Ramaswamy, V.V., Kim, S.S.Y., Russakovsky, O.: Fair attribute classification through latent space de-biasing. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00918"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684\u201310695 (June 2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"14_CR28","unstructured":"Ryu, H.J., Mitchell, M., Adam, H.: Improving smiling detection with race and gender diversity. CoRR abs\/1712.00193 (2017)"},{"key":"14_CR29","unstructured":"Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks. In: ICLR (2020)"},{"key":"14_CR30","unstructured":"Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"14_CR31","unstructured":"Saharia, C., et al.: Photorealistic text-to-image diffusion models with deep language understanding. In: Advance in Neural Information Processing System, vol. 35, pp. 36479\u201336494 (2022)"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Sariyildiz, M.B., Alahari, K., Larlus, D., Kalantidis, Y.: Fake it till you make it: Learning transferable representations from synthetic imagenet clones. In: CVPR 2023\u2013IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00774"},{"key":"14_CR33","unstructured":"Sharmanska, V., Hendricks, L.A., Darrell, T., Quadrianto, N.: Contrastive examples for addressing the tyranny of the majority (2020)"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Tartaglione, E., Barbano, C.A., Grangetto, M.: End: entangling and disentangling deep representations for bias correction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13508\u201313517 (2021)","DOI":"10.1109\/CVPR46437.2021.01330"},{"key":"14_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/978-3-030-58580-8_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Wang","year":"2020","unstructured":"Wang, A., Narayanan, A., Russakovsky, O.: REVISE: a tool for measuring and mitigating bias in visual datasets. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 733\u2013751. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_43"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Wang, X., Lyu, Y., Jing, L.: Deep generative model for robust imbalance classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14124\u201314133 (2020)","DOI":"10.1109\/CVPR42600.2020.01413"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Towards fairness in visual recognition: effective strategies for bias mitigation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00894"},{"key":"14_CR38","unstructured":"Zhang, M., Sohoni, N.S., Zhang, H.R., Finn, C., R\u00e9, C.: Correct-n-contrast: a contrastive approach for improving robustness to spurious correlations. arXiv preprint arXiv:2203.01517 (2022)"},{"key":"14_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.463"},{"key":"14_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, D., Wang, A., Russakovsky, O.: Understanding and evaluating racial biases in image captioning. In: International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01456"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73636-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T10:53:07Z","timestamp":1765018387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73636-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"ISBN":["9783031736353","9783031736360"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73636-0_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,5]]},"assertion":[{"value":"5 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}