{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:47:08Z","timestamp":1767707228828,"version":"3.40.5"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T00:00:00Z","timestamp":1737244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T00:00:00Z","timestamp":1737244800000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11263-024-02321-2","type":"journal-article","created":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T04:18:24Z","timestamp":1737260304000},"page":"3542-3567","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective"],"prefix":"10.1007","volume":"133","author":[{"given":"Jiangmeng","family":"Li","sequence":"first","affiliation":[]},{"given":"Zehua","family":"Zang","sequence":"additional","affiliation":[]},{"given":"Qirui","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Chuxiong","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7985-5743","authenticated-orcid":false,"given":"Wenwen","family":"Qiang","sequence":"additional","affiliation":[]},{"given":"Junge","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Changwen","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,19]]},"reference":[{"key":"2321_CR1","unstructured":"Alemi, A. A., Fischer, I., Dillon, J. V., & Murphy, K. (2016). Deep variational information bottleneck. arXiv preprint arXiv:1612.00410."},{"issue":"3","key":"2321_CR2","first-page":"264","volume":"4","author":"H Al-Mubaid","year":"2007","unstructured":"Al-Mubaid, H. (2007). A learning-classification based approach for word prediction. International Arabian Journal of Information and Technology, 4(3), 264\u2013271.","journal-title":"International Arabian Journal of Information and Technology"},{"key":"2321_CR3","doi-asserted-by":"publisher","unstructured":"Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M. G., LeCun, Y., & Ballas, N. (2023). Self-supervised learning from images with a joint-embedding predictive architecture. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, IEEE, pp. 15619\u201315629, https:\/\/doi.org\/10.1109\/CVPR52729.2023.01499.","DOI":"10.1109\/CVPR52729.2023.01499"},{"key":"2321_CR4","unstructured":"Bachman, P., Hjelm, R. D., & Buchwalter, W. (2019). Learning representations by maximizing mutual information across views. In Wallach HM, Larochelle H, Beygelzimer A, d\u2019Alch\u00e9-Buc F, Fox EB, Garnett R (eds) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp 15509\u201315519, https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/ddf354219aac374f1d40b7e760ee5bb7-Abstract.html."},{"key":"2321_CR5","unstructured":"Bao, H., Dong, L., Piao, S., & Wei, F. (2022). Beit: BERT pre-training of image transformers. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net, https:\/\/openreview.net\/forum?id=p-BhZSz59o4."},{"key":"2321_CR6","unstructured":"Bardes, A., Ponce, J., & LeCun, Y. (2022). Vicreg: Variance-invariance-covariance regularization for self-supervised learning. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net, https:\/\/openreview.net\/forum?id=xm6YD62D1Ub."},{"issue":"2","key":"2321_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/S0167-2681(97)00060-7","volume":"34","author":"H Bester","year":"1998","unstructured":"Bester, H., & G\u00fcth, W. (1998). Is altruism evolutionarily stable? Journal of Economic Behavior & Organization, 34(2), 193\u2013200. https:\/\/doi.org\/10.1016\/S0167-2681(97)00060-7https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167268197000607.","journal-title":"Journal of Economic Behavior & Organization"},{"key":"2321_CR8","unstructured":"Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual."},{"key":"2321_CR9","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020a). Unsupervised learning of visual features by contrasting cluster assignments. In Larochelle H, Ranzato M, Hadsell R, Balcan M, Lin H (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/70feb62b69f16e0238f741fab228fec2-Abstract.html."},{"key":"2321_CR10","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020b). Unsupervised learning of visual features by contrasting cluster assignments. In Larochelle H, Ranzato M, Hadsell R, Balcan M, Lin H (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/70feb62b69f16e0238f741fab228fec2-Abstract.html."},{"key":"2321_CR11","doi-asserted-by":"publisher","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. In 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, IEEE, pp. 9630\u2013964https:\/\/doi.org\/10.1109\/ICCV48922.2021.00951, https:\/\/doi.org\/10.1109\/ICCV48922.2021.00951.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"2321_CR12","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G. E. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, PMLR, Proceedings of Machine Learning Research, vol. 119, pp. 1597\u20131607, http:\/\/proceedings.mlr.press\/v119\/chen20j.html."},{"issue":"1","key":"2321_CR13","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/S11263-023-01852-4","volume":"132","author":"X Chen","year":"2024","unstructured":"Chen, X., Ding, M., Wang, X., Xin, Y., Mo, S., Wang, Y., Han, S., Luo, P., Zeng, G., & Wang, J. (2024). Context autoencoder for self-supervised representation learning. International Journal of Computer Vision, 132(1), 208\u201322. https:\/\/doi.org\/10.1007\/S11263-023-01852-4","journal-title":"International Journal of Computer Vision"},{"key":"2321_CR14","unstructured":"Chuang, C., Robinson, J., Lin, Y., Torralba, A., & Jegelka, S. (2020). Debiased contrastive learning. In Larochelle H, Ranzato M, Hadsell R, Balcan M, Lin H (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/63c3ddcc7b23daa1e42dc41f9a44a873-Abstract.html."},{"key":"2321_CR15","unstructured":"Coates, A., Ng, A. Y., & Lee, H. (2011). An analysis of single-layer networks in unsupervised feature learning. In Gordon GJ, Dunson DB, Dud\u00edk M (eds) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011, JMLR.org, JMLR Proceedings, vol.\u00a015, pp. 215\u2013223, http:\/\/proceedings.mlr.press\/v15\/coates11a\/coates11a.pdf."},{"key":"2321_CR16","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, IEEE Computer Society, pp. 248\u201325https:\/\/doi.org\/10.1109\/CVPR.2009.5206848, https:\/\/doi.org\/10.1109\/CVPR.2009.5206848.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2321_CR17","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for Computational Linguistics, pp 4171\u2013418https:\/\/doi.org\/10.18653\/v1\/n19-1423, https:\/\/doi.org\/10.18653\/v1\/n19-1423.","DOI":"10.18653\/v1\/n19-1423"},{"key":"2321_CR18","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, https:\/\/openreview.net\/forum?id=YicbFdNTTy."},{"key":"2321_CR19","unstructured":"du\u00a0Plessis, M. C., Niu, G., & Sugiyama, M. (2014). Analysis of learning from positive and unlabeled data. In Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp. 703\u2013711, https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/35051070e572e47d2c26c241ab88307f-Abstract.html."},{"key":"2321_CR20","doi-asserted-by":"publisher","unstructured":"Easley, D. A., & Kleinberg, J. M. (2010). Networks, crowds, and markets\u2014Reasoning about a highly connected world. Cambridge University Press. https:\/\/doi.org\/10.1017\/CBO9780511761942http:\/\/www.cambridge.org\/gb\/knowledge\/isbn\/item2705443\/?site_locale=en_GB.","DOI":"10.1017\/CBO9780511761942"},{"key":"2321_CR21","doi-asserted-by":"publisher","unstructured":"Elkan, C., & Noto, K. (2008). Learning classifiers from only positive and unlabeled data. In Li Y, Liu B, Sarawagi S (eds) Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008, ACM, pp. 213\u201322https:\/\/doi.org\/10.1145\/1401890.1401920, https:\/\/doi.org\/10.1145\/1401890.1401920.","DOI":"10.1145\/1401890.1401920"},{"key":"2321_CR22","doi-asserted-by":"crossref","unstructured":"Elkins, J. (2008). Six stories from the end of representation: images in painting, photography, astronomy, microscopy, particle physics, and quantum mechanics, 1980\u20132000. Stanford University Press, http:\/\/www.sup.org\/books\/title\/?id=1341.","DOI":"10.1515\/9781503619395"},{"key":"2321_CR23","unstructured":"Ermolov, A., Siarohin, A., Sangineto, E., & Sebe, N. (2021). Whitening for self-supervised representation learning. In Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, PMLR, Proceedings of Machine Learning Research, vol. 139, pp. 3015\u20133024, http:\/\/proceedings.mlr.press\/v139\/ermolov21a.html."},{"key":"2321_CR24","unstructured":"Federici, M., Dutta, A., Forr\u00e9, P., Kushman, N., & Akata, Z. (2020). Learning robust representations via multi-view information bottleneck. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, https:\/\/openreview.net\/forum?id=B1xwcyHFDr."},{"key":"2321_CR25","doi-asserted-by":"publisher","first-page":"11606","DOI":"10.1016\/j.energy.2019.116064","volume":"189","author":"S Feng Ji","year":"2019","unstructured":"Feng Ji, S., Zhao, D., & Juan, Luo R. (2019). Evolutionary game analysis on local governments and manufacturers\u2019 behavioral strategies: Impact of phasing out subsidies for new energy vehicles. Energy, 189, 11606. https:\/\/doi.org\/10.1016\/j.energy.2019.116064","journal-title":"Energy"},{"issue":"3","key":"2321_CR26","doi-asserted-by":"publisher","first-page":"637","DOI":"10.2307\/2938222","volume":"59","author":"D Friedman","year":"1991","unstructured":"Friedman, D. (1991). Evolutionary games in economics. Econometrica, 59(3), 637\u2013666.","journal-title":"Econometrica"},{"issue":"9","key":"2321_CR27","first-page":"919","volume":"2","author":"RA Gatenby","year":"2003","unstructured":"Gatenby, R. A., & Vincent, T. L. (2003). Application of quantitative models from population biology and evolutionary game theory to tumor therapeutic strategies. Molecular Cancer Therapeutics, 2(9), 919\u2013927.","journal-title":"Molecular Cancer Therapeutics"},{"key":"2321_CR28","doi-asserted-by":"publisher","unstructured":"Gesmundo, A. (2022). A continual development methodology for large-scale multitask dynamic ML systems. CoRR abs\/2209.0732https:\/\/doi.org\/10.48550\/arXiv.2209.07326.","DOI":"10.48550\/arXiv.2209.07326"},{"key":"2321_CR29","unstructured":"Grill, J., Strub, F., Altch\u00e9, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B\u00c1., Guo, Z., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M. (2020). Bootstrap your own latent - A new approach to self-supervised learning. In Larochelle H, Ranzato M, Hadsell R, Balcan M, Lin H (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/f3ada80d5c4ee70142b17b8192b2958e-Abstract.html."},{"key":"2321_CR30","doi-asserted-by":"publisher","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., & Girshick, R. B. (2022a). Masked autoencoders are scalable vision learners. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, IEEE, pp. 15979\u20131598https:\/\/doi.org\/10.1109\/CVPR52688.2022.01553.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"2321_CR31","doi-asserted-by":"publisher","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., & Girshick, R. B. (2022b). Masked autoencoders are scalable vision learners. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, IEEE, pp. 15979\u20131598https:\/\/doi.org\/10.1109\/CVPR52688.2022.01553.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"2321_CR32","doi-asserted-by":"publisher","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. B. (2020). Momentum contrast for unsupervised visual representation learning. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, Computer Vision Foundation \/ IEEE, pp. 9726\u2013973https:\/\/doi.org\/10.1109\/CVPR42600.2020.00975.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2321_CR33","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 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, pp. 770\u201377https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2321_CR34","unstructured":"H\u00e9naff, O. J. (2020). Data-efficient image recognition with contrastive predictive coding. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, PMLR, Proceedings of Machine Learning Research, vol. 119, pp. 4182\u20134192, http:\/\/proceedings.mlr.press\/v119\/henaff20a.html."},{"key":"2321_CR35","unstructured":"Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., & Bengio, Y. (2019). Learning deep representations by mutual information estimation and maximization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, https:\/\/openreview.net\/forum?id=Bklr3j0cKX."},{"issue":"1","key":"2321_CR36","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1006\/game.1998.0691","volume":"28","author":"S Huck","year":"1999","unstructured":"Huck, S., & Oechssler, J. (1999). The indirect evolutionary approach to explaining fair allocations. Games and Economic Behavior, 28(1), 13\u201320. https:\/\/doi.org\/10.1006\/game.1998.0691https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0899825698906911.","journal-title":"Games and Economic Behavior"},{"key":"2321_CR37","unstructured":"Huttenrauch, M., Sosic, A., Neumann, G. (2017). Guided deep reinforcement learning for swarm systems. arXiv preprint arXiv:1709.06011"},{"key":"2321_CR38","unstructured":"Hyvarinen, A. J., & Morioka, H. (2017). Nonlinear ICA of temporally dependent stationary sources. In 20th International Conference on Artificial Intelligence and Statistics."},{"key":"2321_CR39","unstructured":"Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., & et\u00a0al. (2018). Scalable deep reinforcement learning for vision-based robotic manipulation. In Conference on Robot Learning, PMLR, pp. 651\u2013673."},{"issue":"6","key":"2321_CR40","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3390\/E26060466","volume":"26","author":"V Kinakh","year":"2024","unstructured":"Kinakh, V., Drozdova, M., & Voloshynovskiy, S. (2024). MV-MR: Multi-views and multi-representations for self-supervised learning and knowledge distillation. Entropy, 26(6), 46. https:\/\/doi.org\/10.3390\/E26060466","journal-title":"Entropy"},{"key":"2321_CR41","doi-asserted-by":"crossref","unstructured":"Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al\u00a0Sallab, A. A., Yogamani, S., & P\u00e9rez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems.","DOI":"10.1109\/TITS.2021.3054625"},{"key":"2321_CR42","doi-asserted-by":"publisher","unstructured":"Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2020). Big transfer (bit): General visual representation learning. In Vedaldi A, Bischof H, Brox T, Frahm J (eds) Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part V, Springer, Lecture Notes in Computer Science, vol. 12350, pp. 491\u201350https:\/\/doi.org\/10.1007\/978-3-030-58558-7_29.","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"2321_CR43","doi-asserted-by":"publisher","unstructured":"Kornblith, S., Shlens, J., & Le, Q. V. (2019). Do better imagenet models transfer better? In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation \/ IEEE, pp 2661\u2013267https:\/\/doi.org\/10.1109\/CVPR.2019.00277, http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper.html.","DOI":"10.1109\/CVPR.2019.00277"},{"key":"2321_CR44","unstructured":"Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images. University of Toronto, https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf."},{"issue":"1","key":"2321_CR45","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1109\/TETCI.2022.3209394","volume":"7","author":"A Kumar","year":"2023","unstructured":"Kumar, A., Pratap, A., & Singh, A. K. (2023). Generative adversarial neural machine translation for phonetic languages via reinforcement learning. IEEE Transactions Emerging Topics Computational Intelligence, 7(1), 190\u2013199. https:\/\/doi.org\/10.1109\/TETCI.2022.3209394","journal-title":"IEEE Transactions Emerging Topics Computational Intelligence"},{"key":"2321_CR46","unstructured":"Li, S., Liu, Z., Wu, D., Liu, Z., & Li, S. Z. (2021a). Boosting discriminative visual representation learning with scenario-agnostic mixup. CoRR abs\/2111.15454, https:\/\/arxiv.org\/abs\/2111.15454."},{"key":"2321_CR47","unstructured":"Li, Y., Pogodin, R., Sutherland, D. J., & Gretton, A. (2021b). Self-supervised learning with kernel dependence maximization. In Ranzato M, Beygelzimer A, Dauphin YN, Liang P, Vaughan JW (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp. 15543\u201315556, https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/83004190b1793d7aa15f8d0d49a13eba-Abstract.html."},{"key":"2321_CR48","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/J.NEUNET.2023.08.027","volume":"167","author":"J Li","year":"2023","unstructured":"Li, J., Gao, H., Qiang, W., & Zheng, C. (2023). Information theory-guided heuristic progressive multi-view coding. Neural Networks, 167, 415\u201343. https:\/\/doi.org\/10.1016\/J.NEUNET.2023.08.027","journal-title":"Neural Networks"},{"key":"2321_CR49","doi-asserted-by":"publisher","unstructured":"Liu, X., Wang, Z., Li, Y., & Wang, S. (2022). Self-supervised learning via maximum entropy coding. CoRR abs\/2210.1146https:\/\/doi.org\/10.48550\/arXiv.2210.11464.","DOI":"10.48550\/arXiv.2210.11464"},{"issue":"10","key":"2321_CR50","doi-asserted-by":"publisher","first-page":"5105","DOI":"10.1109\/TIP.2019.2914360","volume":"28","author":"D Li","year":"2019","unstructured":"Li, D., Wu, H., Zhang, J., & Huang, K. (2019). Fast A3RL: Aesthetics-aware adversarial reinforcement learning for image cropping. IEEE Transaction on Image Processing, 28(10), 5105\u2013512. https:\/\/doi.org\/10.1109\/TIP.2019.2914360","journal-title":"IEEE Transaction on Image Processing"},{"key":"2321_CR51","unstructured":"Malthus, T. (2023). An essay on the principle of population. In British Politics And The Environment In The Long Nineteenth Century, Routledge, pp. 77\u201384."},{"issue":"1","key":"2321_CR52","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/0022-5193(74)90110-6","volume":"47","author":"J Maynard Smith","year":"1974","unstructured":"Maynard Smith, J. (1974). The theory of games and the evolution of animal conflicts. Journal of Theoretical Biology, 47(1), 209\u201322. https:\/\/doi.org\/10.1016\/0022-5193(74)90110-6https:\/\/www.sciencedirect.com\/science\/article\/pii\/0022519374901106.","journal-title":"Journal of Theoretical Biology"},{"issue":"2","key":"2321_CR53","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1086\/308484","volume":"531","author":"TK Nakamura","year":"2000","unstructured":"Nakamura, T. K. (2000). Statistical mechanics of a collisionless system based on the maximum entropy principle. The Astrophysical Journal, 531(2), 739.","journal-title":"The Astrophysical Journal"},{"key":"2321_CR54","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H. V., Szafraniec, M., Khalidov, V., Fernandez, P., HAZIZA, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P. Y., Li, S. W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., & Bojanowski, P. (2024). DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research https:\/\/openreview.net\/forum?id=a68SUt6zFt."},{"key":"2321_CR55","volume-title":"A course in game theory","author":"MJ Osborne","year":"1994","unstructured":"Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. Cambridge: MIT press."},{"key":"2321_CR56","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl, J. (2009). Causality. Cambridge: Cambridge University Press."},{"key":"2321_CR57","volume-title":"Causal inference in statistics: A primer","author":"J Pearl","year":"2016","unstructured":"Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. Hoboken: Wiley."},{"key":"2321_CR58","unstructured":"Qiang, W., Li, J., Zheng, C., Su, B., & Xiong, H. (2022). Interventional contrastive learning with meta semantic regularizer. In Chaudhuri K, Jegelka S, Song L, Szepesv\u00e1ri C, Niu G, Sabato S (eds) International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, PMLR, Proceedings of Machine Learning Research, vol 162, pp 18018\u201318030, https:\/\/proceedings.mlr.press\/v162\/qiang22a.html."},{"key":"2321_CR59","unstructured":"Robinson, J. D., Chuang, C., Sra, S., & Jegelka, S. (2021). Contrastive learning with hard negative samples. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, https:\/\/openreview.net\/forum?id=CR1XOQ0UTh-."},{"key":"2321_CR60","unstructured":"Saunshi, N., Plevrakis, O., Arora, S., Khodak, M., & Khandeparkar, H. (2019). A theoretical analysis of contrastive unsupervised representation learning. In Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, PMLR, Proceedings of Machine Learning Research, vol.\u00a097, pp. 5628\u20135637, http:\/\/proceedings.mlr.press\/v97\/saunshi19a.html."},{"key":"2321_CR61","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. CoRR abs\/1707.06347, http:\/\/arxiv.org\/abs\/1707.06347."},{"issue":"2","key":"2321_CR62","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/S11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-cam: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128(2), 336\u2013359. https:\/\/doi.org\/10.1007\/S11263-019-01228-7","journal-title":"International Journal of Computer Vision"},{"issue":"2166","key":"2321_CR63","doi-asserted-by":"publisher","first-page":"20190061","DOI":"10.1098\/rsta.2019.0061","volume":"378","author":"J Shalf","year":"2020","unstructured":"Shalf, J. (2020). The future of computing beyond Moore\u2019s law. Philosophical Transactions of the Royal Society A, 378(2166), 20190061.","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"2321_CR64","doi-asserted-by":"publisher","unstructured":"Shvetsova, N., Petersen, F., Kukleva, A., Schiele, B., & Kuehne, H. (2023). Learning by sorting: Self-supervised learning with group ordering constraints. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, IEEE, pp. 16407\u20131641https:\/\/doi.org\/10.1109\/ICCV51070.2023.01508.","DOI":"10.1109\/ICCV51070.2023.01508"},{"issue":"6419","key":"2321_CR65","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1126\/science.aar6404","volume":"362","author":"D Silver","year":"2018","unstructured":"Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science, 362(6419), 1140\u20131144.","journal-title":"Science"},{"issue":"7676","key":"2321_CR66","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017). Mastering the game of go without human knowledge. Nature, 550(7676), 354\u2013359.","journal-title":"Nature"},{"key":"2321_CR67","unstructured":"Sridharan, K., & Kakade, S. M. (2008). An information theoretic framework for multi-view learning. In Servedio RA, Zhang T (eds) 21st Annual Conference on Learning Theory - COLT 2008, Helsinki, Finland, July 9-12, 2008, Omnipress, pp. 403\u2013414, http:\/\/colt2008.cs.helsinki.fi\/papers\/94-Sridharan.pdf."},{"issue":"1","key":"2321_CR68","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/THMS.2022.3225867","volume":"53","author":"Y Sun","year":"2023","unstructured":"Sun, Y., Yuan, B., Xiang, Q., Zhou, J., Yu, J., Dai, D., & Zhou, X. (2023). Intelligent decision-making and human language communication based on deep reinforcement learning in a wargame environment. IEEE Transaction on Human-Machine Systems, 53(1), 201\u201321. https:\/\/doi.org\/10.1109\/THMS.2022.3225867","journal-title":"IEEE Transaction on Human-Machine Systems"},{"key":"2321_CR69","doi-asserted-by":"publisher","unstructured":"Tao, C., Zhu, X., Su, W., Huang, G., Li, B., Zhou, J., Qiao, Y., Wang, X., & Dai, J. (2023). Siamese image modeling for self-supervised vision representation learning. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, IEEE, pp. 2132\u2013214https:\/\/doi.org\/10.1109\/CVPR52729.2023.00212.","DOI":"10.1109\/CVPR52729.2023.00212"},{"issue":"481","key":"2321_CR70","first-page":"429","volume":"103","author":"M Thomas","year":"1991","unstructured":"Thomas, M., & Thomas, Joy A. (1991). Elements of information theory. Publications of the American Statistical Association, 103(481), 429\u2013429.","journal-title":"Publications of the American Statistical Association"},{"key":"2321_CR71","doi-asserted-by":"publisher","unstructured":"Tian, Y., Krishnan, D., & Isola, P. (2020a). Contrastive multiview coding. In Vedaldi A, Bischof H, Brox T, Frahm J (eds) Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XI, Springer, Lecture Notes in Computer Science, vol. 12356, pp. 776\u201379https:\/\/doi.org\/10.1007\/978-3-030-58621-8_45.","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"2321_CR72","unstructured":"Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., & Isola, P. (2020b). What makes for good views for contrastive learning? In Larochelle H, Ranzato M, Hadsell R, Balcan M, Lin H (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/4c2e5eaae9152079b9e95845750bb9ab-Abstract.html."},{"key":"2321_CR73","unstructured":"Tishby, N., Pereira, F. C., & Bialek, W. (2000). The information bottleneck method. arXiv preprint physics\/0004057."},{"key":"2321_CR74","unstructured":"Tsai, Y. H., Wu, Y., Salakhutdinov, R., & Morency, L. (2021). Self-supervised learning from a multi-view perspective. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, https:\/\/openreview.net\/forum?id=-bdp_8Itjwp."},{"key":"2321_CR75","unstructured":"van\u00a0den Oord, A., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. CoRR abs\/1807.03748, http:\/\/arxiv.org\/abs\/1807.03748."},{"key":"2321_CR76","unstructured":"Verma, V., Luong, T., Kawaguchi, K., Pham, H., & Le, Q. V. (2021). Towards domain-agnostic contrastive learning. In Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, PMLR, Proceedings of Machine Learning Research, vol. 139, pp. 10530\u201310541, http:\/\/proceedings.mlr.press\/v139\/verma21a.html."},{"issue":"4","key":"2321_CR77","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/BF00939163","volume":"46","author":"T Vincent","year":"1985","unstructured":"Vincent, T. (1985). Evolutionary games. Journal of Optimization Theory and Applications, 46(4), 605\u2013612.","journal-title":"Journal of Optimization Theory and Applications"},{"issue":"7782","key":"2321_CR78","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/s41586-019-1724-z","volume":"575","author":"O Vinyals","year":"2019","unstructured":"Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019). Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782), 350\u2013354.","journal-title":"Nature"},{"key":"2321_CR79","unstructured":"Wang, Y., Zhang, Q., Wang, Y., Yang, J., & Lin, Z. (2022). Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net, https:\/\/openreview.net\/forum?id=ECvgmYVyeUz."},{"key":"2321_CR80","volume-title":"Evolutionary game theory","author":"JW Weibull","year":"1997","unstructured":"Weibull, J. W. (1997). Evolutionary game theory. Cambridge: MIT press."},{"key":"2321_CR81","unstructured":"Xiao, T., Wang, X., Efros, A. A., & Darrell, T. (2021). What should not be contrastive in contrastive learning. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, https:\/\/openreview.net\/forum?id=CZ8Y3NzuVzO."},{"key":"2321_CR82","doi-asserted-by":"publisher","unstructured":"Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., & Hu, H. (2022). Simmim: a simple framework for masked image modeling. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, IEEE, pp. 9643\u2013965 https:\/\/doi.org\/10.1109\/CVPR52688.2022.00943.","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"2321_CR83","doi-asserted-by":"publisher","unstructured":"Yu, K., Dong, C., Lin, L., & Loy, C. C. (2018). Crafting a toolchain for image restoration by deep reinforcement learning. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, Computer Vision Foundation \/ IEEE Computer Society, pp 2443\u2013245https:\/\/doi.org\/10.1109\/CVPR.2018.00259, http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Yu_Crafting_a_Toolchain_CVPR_2018_paper.html.","DOI":"10.1109\/CVPR.2018.00259"},{"key":"2321_CR84","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., & Deny, S. (2021). Barlow twins: Self-supervised learning via redundancy reduction. In Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, PMLR, Proceedings of Machine Learning Research, vol. 139, pp. 12310\u201312320, http:\/\/proceedings.mlr.press\/v139\/zbontar21a.html."},{"key":"2321_CR85","unstructured":"Zhang, Z., & Sabuncu, M. R. (2018). Generalized cross entropy loss for training deep neural networks with noisy labels. In Bengio S, Wallach HM, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada, pp. 8792\u20138802, https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/f2925f97bc13ad2852a7a551802feea0-Abstract.html."},{"key":"2321_CR86","unstructured":"Zimmermann, R. S., Sharma, Y., Schneider, S., Bethge, M., & Brendel, W. (2021). Contrastive learning inverts the data generating process. In Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, PMLR, Proceedings of Machine Learning Research, vol. 139, pp. 12979\u201312990, http:\/\/proceedings.mlr.press\/v139\/zimmermann21a.html."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02321-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02321-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02321-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T06:57:56Z","timestamp":1746860276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02321-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,19]]},"references-count":86,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2321"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02321-2","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"type":"print","value":"0920-5691"},{"type":"electronic","value":"1573-1405"}],"subject":[],"published":{"date-parts":[[2025,1,19]]},"assertion":[{"value":"11 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}