Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks
This work addresses the challenge of noisy or incomplete labels in supervised learning, which is a common issue in domains like data annotation, but it appears incremental as it builds on existing weak supervision methods.
The paper tackles the problem of limited labeled data by evaluating a representation learning method called Evidence Transfer, which uses external categorical evidence to improve robustness against noisy or incomplete labels. The method proves effective and robust across different levels of incompleteness in two types of incomplete evidence.
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samples by performing automated labelling or crowd-sourcing the annotation procedure. However, they often introduce noise or uncertainty in the labelset, that leads to decreased performance of supervised deep learning methods. On the other hand, weak supervision methods remain robust during noisy labelsets or can be effective even with low amounts of labelled data. In this paper we evaluate the effectiveness of a representation learning method that uses external categorical evidence called "Evidence Transfer", against low amount of corresponding evidence termed as incomplete evidence. Evidence transfer is a robust solution against external unknown categorical evidence that can introduce noise or uncertainty. In our experimental evaluation, evidence transfer proves to be effective and robust against different levels of incompleteness, for two types of incomplete evidence.