Generative Adversarial Networks for anonymous Acneic face dataset generation. (arXiv:2211.04214v1 [cs.CV])

It is well known that the performance of any classification model is
effective if the dataset used for the training process and the test process
satisfy some specific requirements. In other words, the more the dataset size
is large, balanced, and representative, the more one can trust the proposed
model's effectiveness and, consequently, the obtained results. Unfortunately,
large-size anonymous datasets are generally not publicly available in
biomedical applications, especially those dealing with pathological human face
images. This concern makes using …

anonymous arxiv dataset dataset generation face generative adversarial networks networks