{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:24:54Z","timestamp":1767324294976,"version":"3.48.0"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032128393","type":"print"},{"value":"9783032128409","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-12840-9_16","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:21:59Z","timestamp":1767324119000},"page":"237-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On the\u00a0Dangers of\u00a0Bootstrapping Generation for\u00a0Continual Learning and\u00a0Beyond"],"prefix":"10.1007","author":[{"given":"Daniil","family":"Zverev","sequence":"first","affiliation":[]},{"given":"A. Sophia","family":"Koepke","sequence":"additional","affiliation":[]},{"given":"Joao F.","family":"Henriques","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"16_CR1","unstructured":"Achiam, J., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"16_CR2","unstructured":"Alain, G., Bengio, Y.: Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644 (2016)"},{"key":"16_CR3","unstructured":"Alemohammad, S., et al.: Self-consuming generative models go mad. In: ICLR (2024)"},{"key":"16_CR4","unstructured":"Alvarez-Melis, D., Fusi, N.: Geometric dataset distances via optimal transport (2020)"},{"key":"16_CR5","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: ICML (2017)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Bousquet, O., Boucheron, S., Lugosi, G.: Introduction to statistical learning theory. In: Summer School on Machine Learning, pp. 169\u2013207. Springer (2003)","DOI":"10.1007\/978-3-540-28650-9_8"},{"key":"16_CR7","unstructured":"Chaudhry, A., et al.: On tiny episodic memories in continual learning. arXiv preprint arXiv:1902.10486 (2019)"},{"key":"16_CR8","unstructured":"Cong, Y., Zhao, M., Li, J., Wang, S., Carin, L.: Gan memory with no forgetting. In: NeurIPS (2020)"},{"key":"16_CR9","unstructured":"Cui, W., Zhang, L., Wang, Q., Cai, S.: Who said that? benchmarking social media ai detection. arXiv preprint arXiv:2310.08240 (2023)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR11","unstructured":"Gao, R., Liu, W.: Ddgr: continual learning with deep diffusion-based generative replay. In: ICML (2023)"},{"key":"16_CR12","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: NeurIPS (2014)"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Grossberg, S., Grossberg, S.: How does a brain build a cognitive code? neural principles of learning, perception, development, cognition, and motor control, studies of mind and brain (1982)","DOI":"10.1007\/978-94-009-7758-7_1"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Hataya, R., Bao, H., Arai, H.: Will large-scale generative models corrupt future datasets? In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01879"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR16","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 (2017)"},{"key":"16_CR17","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Hochreiter, S.: Long short-term memory. Neural Computation MIT-Press (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"16_CR19","unstructured":"Kaplan, J., et al.: Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020)"},{"key":"16_CR20","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: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"16_CR21","unstructured":"Kemker, R., Kanan, C.: Fearnet: brain-inspired model for incremental learning. CoRR abs\/1711.10563 (2017). http:\/\/arxiv.org\/abs\/1711.10563"},{"key":"16_CR22","unstructured":"Kemker, R., Kanan, C.: Fearnet: brain-inspired model for incremental learning. arXiv preprint arXiv:1711.10563 (2017)"},{"key":"16_CR23","unstructured":"Kouw, W.M., Loog, M.: An introduction to domain adaptation and transfer learning. arXiv preprint arXiv:1812.11806 (2018)"},{"key":"16_CR24","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., D\u00edaz-Rodr\u00edguez, N.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges (2019)","DOI":"10.1016\/j.inffus.2019.12.004"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft coco: common objects in context. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"16_CR28","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"16_CR29","unstructured":"Matatov, H., Qu\u00e9r\u00e9, M.A.L., Amir, O., Naaman, M.: Examining the prevalence and dynamics of AI-generated media in art subreddits. arXiv preprint arXiv:2410.07302 (2024)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"key":"16_CR31","unstructured":"Naveed, H., et al.: A comprehensive overview of large language models. arXiv preprint arXiv:2307.06435 (2023)"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Indian Conference on Computer Vision, Graphics and Image Processing (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Oeldorf, C., Spanakis, G.: Loganv2: conditional style-based logo generation with generative adversarial networks. In: IEEE International Conference on Machine Learning and Applications (2019)","DOI":"10.1109\/ICMLA.2019.00086"},{"key":"16_CR34","doi-asserted-by":"publisher","unstructured":"Prabhu, A., Torr, P.H.S., Dokania, P.K.: GDumb: a simple approach that questions our progress in continual learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 524\u2013540. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_31","DOI":"10.1007\/978-3-030-58536-5_31"},{"key":"16_CR35","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: Incremental Classifier and Representation Learning (2017)","DOI":"10.1109\/CVPR.2017.587"},{"issue":"2","key":"16_CR37","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/09540099550039318","volume":"7","author":"A Robins","year":"1995","unstructured":"Robins, A.: Catastrophic forgetting, rehearsal and pseudorehearsal. Connect. Sci. 7(2), 123\u2013146 (1995)","journal-title":"Connect. Sci."},{"key":"16_CR38","unstructured":"Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., Wayne, G.: Experience replay for continual learning. NeurIPS (2019)"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Scott, D.W.: Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons (2015)","DOI":"10.1002\/9781118575574"},{"key":"16_CR41","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: NeurIPS (2017)"},{"key":"16_CR42","unstructured":"Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., Anderson, R.J.: The curse of recursion: training on generated data makes models forget. arXiv preprint arXiv:2305.17493 (2023)"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., Gal, Y.: AI models collapse when trained on recursively generated data. Nature (2024)","DOI":"10.1038\/s41586-024-07566-y"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"16_CR45","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"16_CR46","unstructured":"Vaze, S., Han, K., Vedaldi, A., Zisserman, A.: Open-set recognition: a good closed-set classifier is all you need? In: ICLR (2022)"},{"key":"16_CR47","unstructured":"Van\u00a0de Ven, G.M., Tolias, A.S.: Generative replay with feedback connections as a general strategy for continual learning. arXiv preprint arXiv:1809.10635 (2018)"},{"key":"16_CR48","unstructured":"van\u00a0der Ven, M., Tolias, A.S.: Generative replay with feedback connections as a general strategy for continual learning. CoRR abs\/1809.10635 (2018). http:\/\/arxiv.org\/abs\/1809.10635"},{"key":"16_CR49","unstructured":"Villalobos, P., Sevilla, J., Heim, L., Besiroglu, T., Hobbhahn, M., Ho, A.: Will we run out of data? an analysis of the limits of scaling datasets in machine learning. arXiv preprint arXiv:2211.04325 (2022)"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, X., Su, H., Zhu, J.: A comprehensive survey of continual learning: theory, method and application. IEEE TPAMI (2024)","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing (2018)","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. IJCV (2024)","DOI":"10.1007\/s11263-024-02117-4"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-12840-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:22:04Z","timestamp":1767324124000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-12840-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032128393","9783032128409"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-12840-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Freiburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dagm-gcpr.de\/year\/2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}