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. Author manuscript; available in PMC: 2019 Jun 26.

Published in final edited form as: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2018 Dec 17;2018:2090–2099. doi: 10.1109/CVPR.2018.00223

Figure 2. — A synthetic example for solving Eq. (3) with λ = 256. Left to right: Objective value in Eq. (4) v.s. #iterations, clustering effect at selected iterations, and weak annotations (30% noise added to ground truth). Red circles () and blue rectangles () indicate samples assiociated with different annotations. Upon convergence at #100, noisy annotations are removed, resulting in distributions closer to ground truth. As can be seen, our method is able to group neighboring samples while preserving their relationship to the weak annotations.

Inline graphic — A synthetic example for solving Eq. (3) with λ = 256. Left to right: Objective value in Eq. (4) v.s. #iterations, clustering effect at selected iterations, and weak annotations (30% noise added to ground truth). Red circles () and blue rectangles () indicate samples assiociated with different annotations. Upon convergence at #100, noisy annotations are removed, resulting in distributions closer to ground truth. As can be seen, our method is able to group neighboring samples while preserving their relationship to the weak annotations.