Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
This addresses the issue of suboptimal training results due to labeling errors for machine learning users, though it is incremental as it builds on existing classifier methods.
The paper tackles the problem of noisy labels in training data for image classification by proposing a classifier-guided visual approach to help users detect and correct errors, which was tested on benchmark datasets and evaluated for usability.
Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training data, we propose a categorization of labeling errors into three different types, based on an analysis of potential pitfalls in label acquisition processes. For each of these types, we present approaches to detect, reason about, and resolve error candidates, as we propose measures and visual guidance techniques to support machine learning users. Our approach has been used to spot errors in well-known machine learning benchmark data sets, and we tested its usability during a user evaluation. While initially developed for images, the techniques presented in this paper are independent of the classification algorithm, and can also be extended to many other types of training data.