{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:31:13Z","timestamp":1778081473271,"version":"3.51.4"},"publisher-location":"Cham","reference-count":78,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200434","type":"print"},{"value":"9783031200441","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20044-1_40","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:12:10Z","timestamp":1666221130000},"page":"702-721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["OccamNets: Mitigating Dataset Bias by\u00a0Favoring Simpler Hypotheses"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0945-3458","authenticated-orcid":false,"given":"Robik","family":"Shrestha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0847-7861","authenticated-orcid":false,"given":"Kushal","family":"Kafle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6412-995X","authenticated-orcid":false,"given":"Christopher","family":"Kanan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"40_CR1","unstructured":"Adeli, E., Zhao, Q., Pfefferbaum, A., Sullivan, E., Fei-Fei, L., Niebles, J.C., Pohl, K.: Bias-resilient neural network. arXiv abs\/1910.03676 (2019)"},{"key":"40_CR2","doi-asserted-by":"publisher","unstructured":"Agrawal, A., Batra, D., Parikh, D., Kembhavi, A.: Don\u2019t just assume; look and answer: overcoming priors for visual question answering. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 4971\u20134980. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00522","DOI":"10.1109\/CVPR.2018.00522"},{"key":"40_CR3","unstructured":"Ahmed, F., Bengio, Y., van Seijen, H., Courville, A.: Systematic generalisation with group invariant predictions. In: International Conference on Learning Representations (2020)"},{"key":"40_CR4","doi-asserted-by":"publisher","unstructured":"Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 6077\u20136086. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00636","DOI":"10.1109\/CVPR.2018.00636"},{"key":"40_CR5","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)"},{"key":"40_CR6","unstructured":"Bolukbasi, T., Chang, K., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 5\u201310 December 2016, pp. 4349\u20134357 (2016)"},{"key":"40_CR7","unstructured":"Cad\u00e8ne, R., Dancette, C., Ben-younes, H., Cord, M., Parikh, D.: RUBi: Reducing unimodal biases for visual question answering. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, BC, Canada, 8\u201314 December 2019, pp. 839\u2013850 (2019)"},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Chen, L., Yan, X., Xiao, J., Zhang, H., Pu, S., Zhuang, Y.: Counterfactual samples synthesizing for robust visual question answering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10800\u201310809 (2020)","DOI":"10.1109\/CVPR42600.2020.01081"},{"key":"40_CR10","unstructured":"Chen, X., Dai, H., Li, Y., Gao, X., Song, L.: Learning to stop while learning to predict. In: International Conference on Machine Learning, pp. 1520\u20131530. PMLR (2020)"},{"key":"40_CR11","unstructured":"Choe, Y.J., Ham, J., Park, K.: An empirical study of invariant risk minimization. In: ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning (2020)"},{"key":"40_CR12","doi-asserted-by":"publisher","unstructured":"Clark, C., Yatskar, M., Zettlemoyer, L.: Don\u2019t take the easy way out: ensemble based methods for avoiding known dataset biases. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, pp. 4069\u20134082. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1418","DOI":"10.18653\/v1\/D19-1418"},{"key":"40_CR13","doi-asserted-by":"publisher","unstructured":"Clark, C., Yatskar, M., Zettlemoyer, L.: Learning to model and ignore dataset bias with mixed capacity ensembles. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 3031\u20133045. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.272","DOI":"10.18653\/v1\/2020.findings-emnlp.272"},{"key":"40_CR14","unstructured":"Creager, E., Jacobsen, J.H., Zemel, R.: Environment inference for invariant learning. In: International Conference on Machine Learning, pp. 2189\u20132200. PMLR (2021)"},{"key":"40_CR15","doi-asserted-by":"publisher","unstructured":"Cui, Y., Jia, M., Lin, T., Song, Y., Belongie, S.J.: Class-balanced loss based on effective number of samples. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 9268\u20139277. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00949","DOI":"10.1109\/CVPR.2019.00949"},{"key":"40_CR16","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: 2009 IEEE conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"40_CR17","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"40_CR18","unstructured":"Duchi, J.C., Hashimoto, T., Namkoong, H.: Distributionally robust losses against mixture covariate shifts. Under review (2019)"},{"key":"40_CR19","unstructured":"Duggal, R., Freitas, S., Dhamnani, S., Horng, D., Sun, J., et al.: ELF: an early-exiting framework for long-tailed classification. arXiv preprint arXiv:2006.11979 (2020)"},{"key":"40_CR20","doi-asserted-by":"publisher","unstructured":"Grand, G., Belinkov, Y.: Adversarial regularization for visual question answering: strengths, shortcomings, and side effects. In: Proceedings of the Second Workshop on Shortcomings in Vision and Language, Minneapolis, Minnesota , pp. 1\u201313. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/W19-1801","DOI":"10.18653\/v1\/W19-1801"},{"key":"40_CR21","unstructured":"Guo, M.H., et al.: Attention mechanisms in computer vision: a survey. arXiv preprint arXiv:2111.07624 (2021)"},{"key":"40_CR22","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE world congress on Computational Intelligence), pp. 1322\u20131328. IEEE (2008)"},{"issue":"9","key":"40_CR23","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR26","unstructured":"Hettinger, C., Christensen, T., Ehlert, B., Humpherys, J., Jarvis, T., Wade, S.: Forward thinking: building and training neural networks one layer at a time. arXiv preprint arXiv:1706.02480 (2017)"},{"key":"40_CR27","unstructured":"Hooker, S., Moorosi, N., Clark, G., Bengio, S., Denton, E.: Characterising bias in compressed models. arXiv preprint arXiv:2010.03058 (2020)"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for mobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"40_CR29","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"40_CR30","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Vedaldi, A.: Gather-excite: exploiting feature context in convolutional neural networks. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"40_CR31","unstructured":"Hu, T.K., Chen, T., Wang, H., Wang, Z.: Triple wins: boosting accuracy, robustness and efficiency together by enabling input-adaptive inference. arXiv preprint arXiv:2002.10025 (2020)"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"40_CR33","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"40_CR34","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: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 9012\u20139020. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00922","DOI":"10.1109\/CVPR.2019.00922"},{"key":"40_CR35","doi-asserted-by":"crossref","unstructured":"Kim, E., Lee, J., Choo, J.: BiaSwap: removing dataset bias with bias-tailored swapping augmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14992\u201315001 (2021)","DOI":"10.1109\/ICCV48922.2021.01472"},{"key":"40_CR36","unstructured":"Krueger, D., et al.: Out-of-distribution generalization via risk extrapolation (REx). In: International Conference on Machine Learning. pp, 5815\u20135826. PMLR (2021)"},{"key":"40_CR37","unstructured":"Lee, C.Y., Gallagher, P.W., Tu, Z.: Generalizing pooling functions in convolutional neural networks: mixed, gated, and tree. In: Artificial Intelligence and Statistics, pp. 464\u2013472. PMLR (2016)"},{"key":"40_CR38","unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562\u2013570. PMLR (2015)"},{"key":"40_CR39","unstructured":"Lee, Y., Yao, H., Finn, C.: Diversify and disambiguate: learning from underspecified data. arXiv preprint arXiv:2202.03418 (2022)"},{"key":"40_CR40","doi-asserted-by":"publisher","unstructured":"Li, Y., Vasconcelos, N.: REPAIR: removing representation bias by dataset resampling. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 9572\u20139581. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00980","DOI":"10.1109\/CVPR.2019.00980"},{"key":"40_CR41","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., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"40_CR42","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":"40_CR43","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"40_CR44","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"6","key":"40_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"40_CR46","doi-asserted-by":"crossref","unstructured":"Mostafa, H., Ramesh, V., Cauwenberghs, G.: Deep supervised learning using local errors. Front. Neurosci. 12, 608 (2018)","DOI":"10.3389\/fnins.2018.00608"},{"key":"40_CR47","unstructured":"Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: training debiased classifier from biased classifier. In: Advances in Neural Information Processing Systems (2020)"},{"key":"40_CR48","unstructured":"Namkoong, H., Duchi, J.C.: Stochastic gradient methods for distributionally robust optimization with F-divergences. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 5\u201310 December 2016, pp. 2208\u20132216 (2016)"},{"key":"40_CR49","unstructured":"N\u00f8kland, A.: Direct feedback alignment provides learning in deep neural networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"40_CR50","unstructured":"N\u00f8kland, A., Eidnes, L.H.: Training neural networks with local error signals. In: International Conference on Machine Learning, pp. 4839\u20134850. PMLR (2019)"},{"key":"40_CR51","unstructured":"Pezeshki, M., Kaba, S.O., Bengio, Y., Courville, A., Precup, D., Lajoie, G.: Gradient starvation: a learning proclivity in neural networks. arXiv preprint arXiv:2011.09468 (2020)"},{"key":"40_CR52","unstructured":"Rahimian, H., Mehrotra, S.: Distributionally robust optimization: a review. arXiv preprint arXiv:1908.05659 (2019)"},{"key":"40_CR53","unstructured":"Ramakrishnan, S., Agrawal, A., Lee, S.: Overcoming language priors in visual question answering with adversarial regularization. In: Bengio, S., Wallach, H.M., 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, Montr\u00e9al, Canada, 3\u20138 December 2018, pp. 1548\u20131558 (2018)"},{"key":"40_CR54","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"40_CR55","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. CoRR abs\/1911.08731 (2019), https:\/\/arxiv.org\/abs\/1911.08731"},{"key":"40_CR56","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. arXiv preprint arXiv:1911.08731 (2019)"},{"key":"40_CR57","unstructured":"Sagawa, S., Raghunathan, A., Koh, P.W., Liang, P.: An investigation of why overparameterization exacerbates spurious correlations. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol. 119, pp. 8346\u20138356. PMLR (2020)"},{"key":"40_CR58","unstructured":"Sanh, V., Wolf, T., Belinkov, Y., Rush, A.M.: Learning from others\u2019 mistakes: avoiding dataset biases without modeling them. arXiv preprint arXiv:2012.01300 (2020)"},{"issue":"5","key":"40_CR59","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1007\/s12559-020-09734-4","volume":"12","author":"S Scardapane","year":"2020","unstructured":"Scardapane, S., Scarpiniti, M., Baccarelli, E., Uncini, A.: Why should we add early exits to neural networks? Cognit. Comput. 12(5), 954\u2013966 (2020)","journal-title":"Cognit. Comput."},{"key":"40_CR60","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"40_CR61","unstructured":"Shah, H., Tamuly, K., Raghunathan, A., Jain, P., Netrapalli, P.: The pitfalls of simplicity bias in neural networks. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"40_CR62","doi-asserted-by":"crossref","unstructured":"Shrestha, R., Kafle, K., Kanan, C.: An investigation of critical issues in bias mitigation techniques. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1943\u20131954 (2022)","DOI":"10.1109\/WACV51458.2022.00257"},{"key":"40_CR63","doi-asserted-by":"publisher","unstructured":"Singh, K.K., Mahajan, D., Grauman, K., Lee, Y.J., Feiszli, M., Ghadiyaram, D.: Don\u2019t judge an object by its context: learning to overcome contextual bias. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 11067\u201311075. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01108","DOI":"10.1109\/CVPR42600.2020.01108"},{"key":"40_CR64","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"40_CR65","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"40_CR66","doi-asserted-by":"crossref","unstructured":"Teerapittayanon, S., McDanel, B., Kung, H.T.: BranchyNet: fast inference via early exiting from deep neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2464\u20132469. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"40_CR67","doi-asserted-by":"crossref","unstructured":"Teney, D., Abbasnejad, E., van den Hengel, A.: Unshuffling data for improved generalization. arXiv preprint arXiv:2002.11894 (2020)","DOI":"10.1109\/ICCV48922.2021.00145"},{"key":"40_CR68","doi-asserted-by":"crossref","unstructured":"Teney, D., Abbasnejad, E., Lucey, S., van den Hengel, A.: Evading the simplicity bias: training a diverse set of models discovers solutions with superior ood generalization. arXiv preprint arXiv:2105.05612 (2021)","DOI":"10.1109\/CVPR52688.2022.01626"},{"key":"40_CR69","unstructured":"Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.: Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems 34 (2021)"},{"key":"40_CR70","doi-asserted-by":"publisher","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 20\u201325 June 2011. pp. 1521\u20131528. IEEE Computer Society (2011). https:\/\/doi.org\/10.1109\/CVPR.2011.5995347","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"40_CR71","doi-asserted-by":"publisher","unstructured":"Utama, P.A., Moosavi, N.S., Gurevych, I.: Towards debiasing NLU models from unknown biases. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7597\u20137610. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.613. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.613","DOI":"10.18653\/v1\/2020.emnlp-main.613"},{"key":"40_CR72","doi-asserted-by":"crossref","unstructured":"Venkataramani, S., Raghunathan, A., Liu, J., Shoaib, M.: Scalable-effort classifiers for energy-efficient machine learning. In: Proceedings of the 52nd Annual Design Automation Conference. pp. 1\u20136 (2015)","DOI":"10.1145\/2744769.2744904"},{"key":"40_CR73","unstructured":"Wo\u0142czyk, M., et al.:: Zero time waste: recycling predictions in early exit neural networks. In: Advances in Neural Information Processing Systems 34 (2021)"},{"key":"40_CR74","unstructured":"Xu, K., Ba, J., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International conference on Machine Learning, pp. 2048\u20132057. PMLR (2015)"},{"key":"40_CR75","doi-asserted-by":"crossref","unstructured":"Yu, W., et al.: Metaformer is actually what you need for vision. arXiv preprint arXiv:2111.11418 (2021)","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"40_CR76","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 335\u2013340 (2018)","DOI":"10.1145\/3278721.3278779"},{"issue":"6","key":"40_CR77","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: A 10 million image database for scene recognition. IEEE T. Pattern Anal. Mach. Intell. 40(6), 1452\u20131464 (2017)","journal-title":"IEEE T. Pattern Anal. Mach. Intell."},{"key":"40_CR78","unstructured":"Zhou, W., et al.: BERT loses patience: fast and robust inference with early exit. In: Advances in Neural Information Processing Systems, vol. 33, pp. 18330\u201318341 (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20044-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:59:59Z","timestamp":1710359999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20044-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200434","9783031200441"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20044-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}