A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels

IEEE Transactions on Cybernetics
Yanshan XiaoBo Liu

Abstract

In multi-instance learning (MIL), labels are associated with bags rather than the instances in the bag. Most of the previous MIL methods assume that each bag has the actual label in the training set. However, from the process of labeling work, the label of a bag is always evaluated by the calculation of the labels obtained from a number of labelers. In the calculation, the weight of each labeler is always unknown and people always assign the weight for each labeler by random or equally, and this may result in the ambiguous labels for the bags, which is called weak labels here. In addition, we always meet the problem of knowledge transfer from the source task to the target task, and this leads to the study of multiple instance transfer learning. In this article, we propose a new framework called transfer learning-based multiple instance learning (TMIL) framework to address the problem of multiple instance transfer learning in which both the source task and the target task contain the weak labels. We first construct a TMIL model with weak labels, which can transfer knowledge from the source task to the target task where both source and target tasks contain weak labels. We then put forward an iterative framework to solve the trans...Continue Reading

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