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We developed a novel SSL approach to capture global consistency andpixel-level local consistencies between differently augmented views of the sameimages to accommodate downstream discriminative and dense predictive tasks. Weadopted the teacher-student architecture used in previous contrastive SSLmethods. In our method, the global consistency is enforced by aggregating thecompressed representations of augmented views of the same image. Thepixel-level consistency is enforced by pursuing similar representations for thesame pixel in differently augmented views. Importantly, we introduced anuncertainty-aware context stabilizer to adaptively preserve the context gapcreated by the two views from different augmentations. Moreover, we used MonteCarlo dropout in the stabilizer to measure uncertainty and adaptively balancethe discrepancy between the representations of the same pixels in differentviews.

Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer