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Golf guided Grad-CAM : attention visualization within golf swings via guided gradient-based class activation mapping
Jiao, Libin ...
Convolutional neural network (CNN)-based methods facilitate data classification but sacrifice physical interpretability due to the complex model architecture and tight inferring integration. The ...interpretability requirement of our prior CNN-based golf classifier motivates us to explain the performance of the predictions and to discover the class-discriminative, significant regions of interest within the golf swings as well. This can be done by casting the 2D Guided Grad-CAMs to a 1D generalization, which is presented in our current research. We then perform the visualization by inspecting the golf predictions and the involved golf dataset using such a custom 1D Guided Grad-CAM, highlight class-discriminative, significant regions of interest, and finally attempt to present potential interpretations. Specifically, we investigate the attention performance and the corresponding potential attributions by visualizing and by evaluating the predictions given by the classifier and the golf swings from five perspectives, including attention consistency within particular classes, the inspections of misclassified swings, Guided Grad-CAM visualizations at different layers, and the attention shift with respect to temporal resolutions and with respect to sensor usages. We conclude that our visual inspections explain our previous classification performance, that the classdiscriminative, significant features can be captured, and that every single prediction has its reasonable interpretation, in terms of the comprehensive experiments. Such exploration can provide a potential possibility of associating the critical regions and features with the physical movements of golf players, which can possibly contribute to golf training. Relevant code files are available at https://github.com/92xianshen/golf-guided-gradcam.
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