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This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
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Sample selection bias as a specification error
Heckman, J. J · 1979
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Heckman, J · 1990
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Seung, H. S., Opper, M., and Sompolinsky, H · 1992
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A sequential algorithm for training text classifiers
Lewis, D. D. and Gale, W. A · 1994
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Support vector machine active learning with applications to text classification
Tong, S. and Koller, D · 2001
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Incorporating diversity in active learning with support vector machines
Brinker, K · 2003
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Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., and He, K · 2003
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Big self-supervised models are strong semi-supervised learners
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The relationship between precision-recall and roc curves
Davis, J. and Goadrich, M · 2006
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Batch mode active learning and its application to medical image classification
Hoi, S. C., Jin, R., Zhu, J., and Lyu, M. R · 2006
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Active learning for regression based on query by committee
Burbidge, R., Rowland, J. J., and King, R. D · 2007
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Hierarchical sampling for active learning
Dasgupta, S. and Hsu, D · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
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Active learning literature survey
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Transparent active learning for robots
Chao, C., Cakmak, M., and Thomaz, A. L · 2010
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Two faces of active learning
Dasgupta, S · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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A unifying view on dataset shift in classification
Moreno-Torres, J. G., Raeder, T., Alaiz-RodríGuez, R., Chawla, N. V., and Herrera, F · 2012
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Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
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Probabilistic modeling of deep features for out-of-distribution and adversarial detection
Ahuja, N. A., Ndiour, I., Kalyanpur, T., and Tickoo, O · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Kirsch, A., van Amersfoort, J., and Gal, Y · 2019
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He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
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Deep active learning for named entity recognition
Shen, Y., Yun, H., Lipton, Z. C., Kronrod, Y., and Anandkumar, A · 2017
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A deeper look at dataset bias
Tommasi, T., Patricia, N., Caputo, B., and Tuytelaars, T · 2017
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Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Yang, L., Zhang, Y., Chen, J., Zhang, S., and Chen, D. Z · 2017
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Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2019
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Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 2019
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A framework for understanding unintended consequences of machine learning, 2019
Suresh, H. and Guttag, J. V · 2019
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Learning loss for active learning
Yoo, D. and Kweon, I. S · 2019
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D · 2020
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Improving model calibration with accuracy versus uncertainty optimization
Krishnan, R. and Tickoo, O · 2020
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Deep active learning: Unified and principled method for query and training
Shui, C., Zhou, F., Gagné, C., and Wang, B · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J., Mo, S., Jeong, J., and Shin, J · 2020
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G. G., Krishnan, R., Stanley, J., Tickoo, O., et al · 2021
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On statistical bias in active learning: How and when to fix it
Farquhar, S., Gal, Y., and Rainforth, T · 2021
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