Abstract / truncated to 115 words (read the full abstract)

Over the last decade, deep learning methods have gained increasing traction in indus trial applications, ranging from image-based automated quality control, over signal enhancement to condition monitoring tasks. While deep learning has immensely in creased the performance and capabilities of machine learning models, it also increased the vulnerability of those models. Moreover, these models require vast amounts of data in order to generalize well. This is problematic for industrial applications since the amount of available data is often limited and most practical applications outside the field of big data have to deal with scarce data. This is especially true for supervised tasks, as creating labeled datasets often involves expensive expert labor. In contrast, big data ... toggle 3 keywords

deep learning – inductive biases – edge computing

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