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An end-to-end machine learning approach for classifying rolling contact fatigue (RCF) defects utilizing defect images is presented and evaluated. The core component of this approach is the use of a fine-tuned AlexNet architecture (FT-AlexNet), which is a well-known pre-trained deep Convolutional Neural Network (DCNN). Through comparing the FT-AlexNet method with two classical two-step classification methods that include a feature extraction step and then train a classifier, it was found thatdoi:10.1109/iccais.2017.8217573 dblp:conf/iccais/Chen-McCaigHB17 fatcat:sthiusvdzbfgzkn523saqmmrr4