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Weakly supervised data are widespread and have attracted much attention.However, since label quality is often difficult to guarantee, sometimes the useof weakly supervised data will lead to unsatisfactory performance, i.e.,performance degradation or poor performance gains. Moreover, it is usually notfeasible to manually increase the label quality, which results in weaklysupervised learning being somewhat difficult to rely on. In view of thiscrucial issue, this paper proposes a simple and novel weakly supervisedlearning framework. We guide the optimization of label quality through a smallamount of validation data, and to ensure the safeness of performance whilemaximizing performance gain. As validation set is a good approximation fordescribing generalization risk, it can effectively avoid the unsatisfactoryperformance caused by incorrect data distribution assumptions. We formalizethis underlying consideration into a novel Bi-Level optimization and give aneffective solution. Extensive experimental results verify that the newframework achieves impressive performance on weakly supervised learning with asmall amount of validation data.

Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness