{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:03:43Z","timestamp":1780589023813,"version":"3.54.1"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729065","type":"print"},{"value":"9783031729072","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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\/978-3-031-72907-2_7","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:22:17Z","timestamp":1730301737000},"page":"107-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A High-Quality Robust Diffusion Framework for\u00a0Corrupted Dataset"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0996-0472","authenticated-orcid":false,"given":"Quan","family":"Dao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3553-5833","authenticated-orcid":false,"given":"Binh","family":"Ta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tung","family":"Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3120-4036","authenticated-orcid":false,"given":"Anh","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"7_CR1","unstructured":"Akbari, A., Awais, M., Bashar, M., Kittler, J.: How does loss function affect generalization performance of deep learning? Application to human age estimation. In: International Conference on Machine Learning, pp. 141\u2013151. PMLR (2021)"},{"key":"7_CR2","unstructured":"Altschuler, J., Weed, J., Rigollet, P.: Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. In: Advances in Neural Information Processing Systems, pp. 1964\u20131974 (2017)"},{"key":"7_CR3","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223. PMLR (2017)"},{"key":"7_CR4","unstructured":"Balaji, Y., Chellappa, R., Feizi, S.: Robust optimal transport with applications in generative modeling and domain adaptation. In: NeurIPS (2020)"},{"issue":"11","key":"7_CR5","doi-asserted-by":"publisher","first-page":"3090","DOI":"10.1016\/j.jfa.2018.03.008","volume":"274","author":"L Chizat","year":"2018","unstructured":"Chizat, L., Peyr\u00e9, G., Schmitzer, B., Vialard, F.X.: Unbalanced optimal transport: dynamic and Kantorovich formulations. J. Funct. Anal. 274(11), 3090\u20133123 (2018)","journal-title":"J. Funct. Anal."},{"key":"7_CR6","unstructured":"Choi, J., Choi, J., Kang, M.: Generative modeling through the semi-dual formulation of unbalanced optimal transport. arXiv preprint arXiv:2305.14777 (2023)"},{"key":"7_CR7","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Gordon, G., Dunson, D., Dud\u00edk, M. (eds.) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol.\u00a015, pp. 215\u2013223. PMLR, Fort Lauderdale, FL, USA (2011)"},{"key":"7_CR8","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, pp. 2292\u20132300 (2013)"},{"key":"7_CR9","unstructured":"Dao, Q., Phung, H., Nguyen, B., Tran, A.: Flow matching in latent space. arXiv preprint arXiv:2307.08698 (2023)"},{"key":"7_CR10","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems, vol. 34, pp. 8780\u20138794 (2021)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis (2020)","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"7_CR12","unstructured":"Gallou\u00ebt, T., Ghezzi, R., Vialard, F.X.: Regularity theory and geometry of unbalanced optimal transport. arXiv preprint arXiv:2112.11056 (2021)"},{"key":"7_CR13","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"7_CR14","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"7_CR15","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (2020)"},{"key":"7_CR16","unstructured":"Huang, C.W., Lim, J.H., Courville, A.C.: A variational perspective on diffusion-based generative models and score matching. In: Advances in Neural Information Processing Systems, vol. 34, pp. 22863\u201322876 (2021)"},{"key":"7_CR17","unstructured":"Janati, H., Cuturi, M., Gramfort, A.: Spatio-temporal alignments: optimal transport through space and time. arXiv preprint arXiv:1910.03860 (2019)"},{"key":"7_CR18","unstructured":"Jiang, Y., Chang, S., Wang, Z.: TransGAN: two pure transformers can make one strong GAN, and that can scale up. In: Advances in Neural Information Processing Systems, vol. 34, pp. 14745\u201314758 (2021)"},{"key":"7_CR19","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (2018)"},{"key":"7_CR20","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Advances in Neural Information Processing Systems (2020)"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"7_CR23","unstructured":"Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. In: Advances in Neural Information Processing Systems, vol. 34, pp. 21696\u201321707 (2021)"},{"key":"7_CR24","unstructured":"Kodali, N., Abernethy, J., Hays, J., Kira, Z.: On convergence and stability of GANs. arXiv preprint arXiv:1705.07215 (2017)"},{"key":"7_CR25","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. University of Toronto (2012)"},{"key":"7_CR26","unstructured":"Kynk\u00e4\u00e4nniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"7_CR27","unstructured":"Le, K., Nguyen, H., Nguyen, Q., Ho, N., Pham, T., Bui, H.: On robust optimal transport: computational complexity and barycenter computation (2021)"},{"key":"7_CR28","unstructured":"Le, T., Phung, H., Nguyen, T., Dao, Q., Tran, N., Tran, A.: Anti-DreamBooth: protecting users from personalized text-to-image synthesis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2023)"},{"key":"7_CR29","unstructured":"Lou, A., Ermon, S.: Reflected diffusion models. arXiv preprint arXiv:2304.04740 (2023)"},{"key":"7_CR30","unstructured":"Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: DPM-solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps. arXiv preprint arXiv:2206.00927 (2022)"},{"key":"7_CR31","unstructured":"Meng, C., et al.: SDEdit: guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)"},{"key":"7_CR32","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Park, J., Kim, Y.: StyleFormer: transformer based generative adversarial networks with style vector (2021)","DOI":"10.1109\/CVPR52688.2022.00878"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Peyr\u00e9, G., Cuturi, M.: Computational optimal transport. Found. Trends\u00ae Mach. Learn. 11(5-6), 355\u2013607 (2019)","DOI":"10.1561\/2200000073"},{"key":"7_CR35","unstructured":"Pham, K., Le, K., Ho, N., Pham, T., Bui, H.: On unbalanced optimal transport: an analysis of Sinkhorn algorithm (2020)"},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"Phung, H., Dao, Q., Tran, A.: Wavelet diffusion models are fast and scalable image generators. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10199\u201310208 (2023)","DOI":"10.1109\/CVPR52729.2023.00983"},{"key":"7_CR37","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022)"},{"key":"7_CR38","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, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"7_CR39","unstructured":"Rout, L., Korotin, A., Burnaev, E.: Generative modeling with optimal transport maps. arXiv preprint arXiv:2110.02999 (2021)"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: DreamBooth: fine tuning text-to-image diffusion models for subject-driven generation (2022)","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"7_CR41","unstructured":"Saharia, C., et\u00a0al.: Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487 (2022)"},{"key":"7_CR42","unstructured":"Salimans, T., Zhang, H., Radford, A., Metaxas, D.: Improving GANs using optimal transport. arXiv preprint arXiv:1803.05573 (2018)"},{"key":"7_CR43","unstructured":"Sanjabi, M., Ba, J., Razaviyayn, M., Lee, J.D.: On the convergence and robustness of training GANs with regularized optimal transport. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"7_CR44","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning (2015)"},{"key":"7_CR45","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)"},{"key":"7_CR46","unstructured":"Song, Y., Durkan, C., Murray, I., Ermon, S.: Maximum likelihood training of score-based diffusion models. In: Advances in Neural Information Processing Systems, vol. 34, pp. 1415\u20131428 (2021)"},{"key":"7_CR47","unstructured":"Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Advances in Neural Information Processing Systems (2019)"},{"key":"7_CR48","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (2021)"},{"key":"7_CR49","unstructured":"Vacher, A., Vialard, F.X.: Stability and upper bounds for statistical estimation of unbalanced transport potentials. arXiv preprint arXiv:2203.09143 (2022)"},{"key":"7_CR50","unstructured":"Vahdat, A., Kautz, J.: NVAE: a deep hierarchical variational autoencoder. In: Advances in Neural Information Processing Systems (2020)"},{"key":"7_CR51","doi-asserted-by":"crossref","unstructured":"Villani, C.: Optimal transport: old and new (2008)","DOI":"10.1007\/978-3-540-71050-9"},{"key":"7_CR52","unstructured":"Wang, W., et al.: Semantic image synthesis via diffusion models. arXiv preprint arXiv:2207.00050 (2022)"},{"key":"7_CR53","unstructured":"Wang, Z., Zheng, H., He, P., Chen, W., Zhou, M.: Diffusion-GAN: training GANs with diffusion. arXiv preprint arXiv:2206.02262 (2022)"},{"key":"7_CR54","unstructured":"Xiao, Z., Kreis, K., Kautz, J., Vahdat, A.: VAEBM: a symbiosis between variational autoencoders and energy-based models. In: International Conference on Learning Representations (2021)"},{"key":"7_CR55","unstructured":"Xiao, Z., Kreis, K., Vahdat, A.: Tackling the generative learning trilemma with denoising diffusion GANs. In: International Conference on Learning Representations (ICLR) (2022)"},{"key":"7_CR56","unstructured":"Yang, K.D., Uhler, C.: Scalable unbalanced optimal transport using generative adversarial networks. arXiv preprint arXiv:1810.11447 (2018)"},{"key":"7_CR57","unstructured":"Zhang, Q., Chen, Y.: Fast sampling of diffusion models with exponential integrator. arXiv preprint arXiv:2204.13902 (2022)"}],"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\/978-3-031-72907-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:25:40Z","timestamp":1730301940000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72907-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031729065","9783031729072"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72907-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 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"}}]}}