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Learning From Noisy Labels by Regularized Estimation of Annotator Confusion.

, , , , and . CVPR, page 11244-11253. Computer Vision Foundation / IEEE, (2019)

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Instance Segmentation of Indoor Scenes Using a Coverage Loss., , and . ECCV (1), volume 8689 of Lecture Notes in Computer Science, page 616-631. Springer, (2014)Learning From Noisy Labels by Regularized Estimation of Annotator Confusion., , , , and . CVPR, page 11244-11253. Computer Vision Foundation / IEEE, (2019)Case for Automated Detection of Diabetic Retinopathy., , , and . AAAI Spring Symposium: Artificial Intelligence for Development, AAAI, (2010)Instance Segmentation of RGBD Scenes.. New York University, USA, (2015)Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models., , , , and . NIPS, page 1231-1239. Curran Associates, Inc., (2009)Indoor scene segmentation using a structured light sensor., and . ICCV Workshops, page 601-608. IEEE Computer Society, (2011)Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth., , , , , , , , , and 1 other author(s). ICLR, OpenReview.net, (2020)The Devil is in the Decoder: Classification, Regression and GANs., , , , , , and . Int. J. Comput. Vis., 127 (11-12): 1694-1706 (2019)ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations., , , and . ECCV (10), volume 11214 of Lecture Notes in Computer Science, page 681-696. Springer, (2018)Im2Calories: Towards an Automated Mobile Vision Food Diary., , , , , , , , , and . ICCV, page 1233-1241. IEEE Computer Society, (2015)