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Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth., , , , , , , , , and 1 other author(s). ICLR, OpenReview.net, (2020)An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks., , , , , , , and . CoRR, (2016)3D facial model synthesis using coupled dictionaries., , and . ICIP, page 3896-3900. IEEE, (2015)Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition., , , , , , and . CoRR, (2018)Triplet Similarity Embedding for Face Verification., , and . CoRR, (2016)Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans., , , , , , , and . IEEE Signal Process. Mag., 35 (1): 66-83 (2018)Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks., , , , , , , and . Int. J. Comput. Vis., 126 (2-4): 272-291 (2018)MetaReg: Towards Domain Generalization using Meta-Regularization., , and . NeurIPS, page 1006-1016. (2018)Is CLIP Fooled by Optical Illusions?, , and . Tiny Papers @ ICLR, OpenReview.net, (2023)Towards the design of an end-to-end automated system for image and video-based recognition., , , , , , and . ITA, page 1-7. IEEE, (2016)