Generative Adversarial Networks for anonymous Acneic face dataset generation

Understand

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 deep-learning-based approaches challenging to deploy and difficult to reproduce or verify some published results.