This paper addresses the domain of facial super-resolution, focusing on the task of enhancing low-resolution facial images for real-world applications. Incremental Super-Resolution using Generative Adversarial Networks with Knowledge Distillation (ISR-KD) is a unified framework capable of incrementally learning and adapting to new, unseen data—a pivotal aspect for applications where data continuously evolves.

The authors approach incremental learning and knowledge distillation in computer vision to overcome challenges like class imbalance and catastrophic forgetting. It then navigates through the landscape of face super-resolution, providing an extensive review of deep learning models and techniques employed for enhancing facial image resolution, showcasing a variety of methods from PCA-SRGAN to supervised pixel-wise GAN.

Detailing the proposed methodology in ISR-KD, they outline the incremental super-resolution process, emphasizing the image degradation function and formalization of incremental learning coupled with knowledge distillation. The Incremental FSR Generator is introduced along with the role of knowledge distillation in mitigating catastrophic forgetting.