Towards efficient image and video style transfer via distillation and learnable feature transformation

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Jing Huo, Meihao Kong, Wenbin Li, Jing Wu, Yu-Kun Lai, Yang Gao

Published: 01 Jan 2024, Last Modified: 13 Nov 2024Comput. Vis. Image Underst. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•A knowledge distillation method to compress VGG-19 based backbone is proposed.•A light weight feature transformation module for flexible style transfer is proposed.•A temporal consistency loss to maintain video style transfer stability is proposed.•A current smallest style transfer model is derived, only 2.67 MB.•The final model can perform style transfer at 167 FPS on a 2080Ti GPU.