Microdosing: Knowledge Distillation for GAN based Compression
arXiv Preprint Archive January 7, 2022 Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah et al.
Learned image and video compression has advanced with Generative Adversarial Networks, but current models are large and computationally demanding, limiting practical use. This paper shows that knowledge distillation can produce image decoders with far fewer parameters while maintaining performance. The approach includes sequence specialization using side information for image coding and extends the benefits to video compression. The method reduces model size by a factor of 20 and cuts decoding time by 50%.