Changes

A total of 6 proGAN models were built and trained, each one with the required number of ''straight-through'' and ''fade-in'' model stages, to generate several typologies of synthesized images at the desired target resolution, belonging to ''missing'' and ''tombstoning'' classes, more specifically ''full'' images (512 × 512 resolution), and ''upper'' and ''/ lower'' soldering region images (256 × 256 resolution). The training was executed mainly on cloud, initially with the free services provided by [https://colab.research.google.com/ Google Colab ] and finally on with [https://aws.amazon.com/it/sagemaker/ AWS SageMaker].

Discriminators and generators were trained both with a learn rate of 5 × 10^-4 for all fade-in stages and with a smaller learn rate of 1 × 10^-4 for all ''straight-through'' stages, in order to guarantee a smooth and slow enough fine-tuning for all layers. Each stage of each created model was trained for 100 epochs, with the sole exception of the last stage for the 512×512 full images, which needed 50 more epochs to generate satisfying results. The batch size is progressively reduced as the resolution increases, starting from a batch of 64 images for the first three resolutions (4 × 4, 8 × 8 and 16 × 16 resolutions), decreasing to 32 (32 × 32, and 64 × 64 resolutions) and 16 (128 × 128 and 256 × 256 resolutions). In the last stage for full typology (512 × 512), batch size is further reduced to 8 images for each train step.