  Facial super-resolution/hallucination is an important area of research that
seeks to enhance low-resolution facial images for a variety of applications.
While Generative Adversarial Networks (GANs) have shown promise in this area,
their ability to adapt to new, unseen data remains a challenge. This paper
addresses this problem by proposing an incremental super-resolution using GANs
with knowledge distillation (ISR-KD) for face to cartoon. Previous research in
this area has not investigated incremental learning, which is critical for
real-world applications where new data is continually being generated. The
proposed ISR-KD aims to develop a novel unified framework for facial
super-resolution that can handle different settings, including different types
of faces such as cartoon face and various levels of detail. To achieve this, a
GAN-based super-resolution network was pre-trained on the CelebA dataset and
then incrementally trained on the iCartoonFace dataset, using knowledge
distillation to retain performance on the CelebA test set while improving the
performance on iCartoonFace test set. Our experiments demonstrate the
effectiveness of knowledge distillation in incrementally adding capability to
the model for cartoon face super-resolution while retaining the learned
knowledge for facial hallucination tasks in GANs.
