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.

提出了一种使用GAN和知识蒸馏的增量超分辨率方法（ISR-KD）来实现面部卡通化，旨在开发用于面部超分辨率的新型统一框架，能够处理不同的设置，包括不同类型的面部，如卡通面部和不同级别的细节。实验证明，知识蒸馏能够有效地增加模型对卡通面部超分辨率的能力，并保留GAN中面部幻觉任务的学到的知识。

面向卡通人脸的增量超分辨率技术研究：基于知识蒸馏