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\textit{Multiple Instance Learning} (MIL) is concerned with learning frombags of instances, where only bag labels are given and instance labels areunknown. Existent approaches in this field were mainly designed for thebag-level label prediction (predict labels for bags) but not the instance-level(predict labels for instances), with the task loss being only defined at thebag level. This restricts their application in many tasks, where theinstance-level labels are more interested. In this paper, we propose a novelalgorithm, whose loss is specifically defined at the instance level, to addressinstance-level label prediction in MIL. We prove that the loss of thisalgorithm can be unbiasedly and consistently estimated without using instancelabels, under the i.i.d assumption. Empirical study validates the abovestatements and shows that the proposed algorithm can achieve superiorinstance-level and comparative bag-level performance, compared tostate-of-the-art MIL methods. In addition, it shows that the proposed methodcan achieve similar results as the fully supervised model (trained withinstance labels) for label prediction at the instance level.

Address Instance-level Label Prediction in Multiple Instance Learning