Machine learning has been applied successfully for the last few decades in vast range of domains that include computer vision, natural language processing, pattern recognition, remote sensing etc. Machine learning techniques aid in developing predictive models, clustering, object segmentation, pattern classifications and so on. The data from which these machine learning systems learn governs several aspects such as performance and model complexity. However, data is either labeled by humans or automated methods, or a combination of both. Introduction of errors in labels happen at any stage of labeling that lead to a phenomenon called label noise. This is one of the most challenging issue to deal with as performance and model complexity is closely related to it. In many cases, label noise is ignored and assumed to be absent. But in reality, most of the times empirical data is error prone to label noise and explicit consideration of such noise should be given.

This Special Issue is intended to present discussions, techniques that are used to deal with label noise in different types of data, i.e., images, audio, video, text etc. in the arena of dense prediction, classification, regression, object detection and used in various disciplines such as medicine, finance, remote sensing, ecology, industrial control systems etc. Topics include but are not limited to the following areas:

• Sources of label noise in data from different disciplines
• Quantification of label noise
• Label noise robust learning methods
• Label noise injection methods
• Label noise filters
• Comparison of different labeling strategies, e.g., automated and crowd sourced labeling
• Effects of label noise on performance and model complexity
• Self and semi-supervised methods for learning from noisy data

Dr. M. Rashedur Rahman
Dr. Ken Barker
Dr. Luís Paulo F. Garcia
Dr. Nabeel Mohammed
Dr. Sifat Momen
Guest Editors

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