A new study developed a snore-source classification model that uses STFT spectrograms, pretrained CNN features, and an L2-regularized SVM to identify where snoring originates in the upper airway.

Abstract: Supervised learning approaches are widely used for driving style classification; however, they often require a large amount of labeled training data, which is usually scarce in a real-world ...

Abstract: The automated identification of wood surface defects is crucial for maintaining product quality and maximizing resource efficiency in the timber industry. Yet, conventional deep learning ...

This is a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is ...

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