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As the following images from the paper shows, during training time you create patches from larger images. These patches have no defects and can therefore be seen as 'good' images. To get the accompanying 'bad' images with defects you synthetically generate these defects on the 'good' images. Your data generator should therefore follow the following steps: ...


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Having N encoders would multiply the number of parameters of the encoder side N times, which would lead to different learning dynamics, so the results would probably not the same, and you may incur in overfitting. I don't think there is a case where that would make more sense than having a single encoder.


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Why don't you use a lower number of filters in the last convolution? Instead of 128 you can just choose whatever number you want, e.g. 10. Also, normally after the convolutional (and pooling layers), you flatten the output (therefore losing the spatial information) and then project with a dense layer onto the final representation space. You can control the ...


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