A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications
Niclas Simmler, Pascal Sager, Philipp Andermatt, Ricardo Chavarriaga, F.-P. Schilling, Matthias Rosenthal, Thilo Stadelmann
Abstract
When applying deep learning methods in an industrial vision application, they often fall short of the performance shown in a clean and controlled lab environment due to data quality issues. Few would consider the actual labels as a driving factor, yet inaccurate label data can impair model performance significantly. However, being able to mitigate inaccurate or incomplete labels might also be a cost-saver for real-world projects. Here, we survey state-of-the-art deep learning approaches to resolve such missing labels, noisy labels, and partially labeled data in the prospect of an industrial vision application. We systematically present un-, weakly, and semi-supervised approaches from 'A' like anomaly detection to 'Z' like zero-shot classification to resolve these challenges by embracing them.