OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation
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DOI: 10.1109/tmm.2022.3230331
Abstract: Face representation in the wild is extremely hard due to the large scale face variations. Some deep convolutional neural networks (CNNs) have been developed to learn discriminative feature by designing properly margin-based losses, which perform… read more here.
Keywords: samples guided; face; feature; face representation ... See more keywords