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Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation
In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss.
Transform consistency for learning with noisy labels
Previous methods such as Coteaching and JoCoR introduce two different networks to select clean samples out of the noisy ones and only use these clean ones to train the deep models.
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