This video explains the paper "GAN Compression: Efficient Architectures for Interactive Conditional GANs"! This technique adapts Knowledge Distillation for the GAN framework by copying intermediate features from the teacher to student generator, transferring the pre-trained teacher discriminator, and structuring image-to-image translation problems in the "paired" setting by using the teacher generator image as the ground truth image. This paper also explores the use of One-Shot Neural Architecture Search to find an efficient architecture for the student generator network!
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Paper Links:
GAN Compression: [ Ссылка ]
GAN Compression Video Demo from Authors: [ Ссылка ]
Pix2Pix: [ Ссылка ]
CycleGAN: [ Ссылка ]
GauGAN: [ Ссылка ]
AVID: [ Ссылка ]
DermGAN: [ Ссылка ]
SimGAN: [ Ссылка ]
MobileNets: [ Ссылка ]
One-Shot Neural Architecture Search: [ Ссылка ]
Intro Music: "Runs" from Unminus
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