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Autonomous Stabilization of Retinal Videos for Streamlining Assessment of Spontaneous Venous Pulsations
Both of the evaluations support its effectiveness in facilitating the observation of SVPs.
Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound
Benefited from this new perspective, we first propose a new deep semi-supervised learning framework called Semi-supervised Learning by Empirical Distribution Alignment (SLEDA), in which existing technologies from the domain adaptation community can be readily used to address the semi-supervised learning problem through reducing the empirical distribution distance between labeled and unlabeled data.
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