Changes

The synthesized images can be effectively used to train a SMC defects classification model for a future ML-based PCB-AVI application, only if they have a similar probability distribution function with respect to the original ones. In particular, it is interesting to verify if there is a clear separation in the data. To this end, it is possible to employ the t-distributed stochastic neighbor embedding (t-SNE), which is a ML algorithm for visualization, based on Stochastic Neighbor Embedding (SNE) algorithm. Since the way this algorithm operates is computationally expensive, it is highly recommended to use another dimensionality reduction technique before applying t-SNE algorithm, in order to reduce the number of dimensions to a reasonable amount. For this purpose, it is possible to employ an autoencoder (AE).

The values obtained by applying the t-SNE algorithm on the compressed data are displayed into several 3D-plots reported below respectively for ''missing'' and ''tombstoning'' synthesized images. Interestingly, the 4 plots related to full typology do not show a separation in the data between ''fakes'' (red dots) and ''reals'' (blue dots), sign that the generated images do not differ too much from the original ones, used to train the proGANs.