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In this paper we want to reuse the encoder weights of SCAEs that have been trained.
In autoencoders, encoders, also called recognition networks, are responsible for transforming inputs into internal representations.
In the proposed method, SCAE, since the encoder is composed of a convolutional layer, it can be considered as an effective image feature extractor.
Therefore, we encode the data input to the classifier through encoder weights and then proceed with learning.
Because a feature vector in which image information is preserved can be obtained through the encoder, defect classification is performed with a softmax classifier without using a classifier model with a complex structure.
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IEEE/ICACT20220181 Slide.13
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