PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models

Photorealistic style transfer entails transferring the style of a reference image to another image so the result seems like a plausible photo. Our work is inspired by the ob-servation that existing models are slow due to their large sizes. We introduce PCA-based knowledge distillation to distill lightweight models and show it is motivated by the-ory. To our knowledge, this is the first knowledge dis-tillation method for photorealistic style transfer. Our ex-periments demonstrate its versatility for use with differ-ent backbone architectures, VGG and MobileNet, across six image resolutions. Compared to existing models, our top-performing model runs at speeds 5-20x faster using at most 1% of the parameters. Additionally, our dis-tilled models achieve a better balance between stylization strength and content preservation than existing models. To support reproducing our method and models, we share the code at https://github.com/chiutaiyin/PCA-Knowledge-Distillation.