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With the continuous development of deep-learning technology, ever more advanced face-swapping methods are being proposed. Recently, face-swapping methods based on generative adversarial networks (GANs) have realized many-to-many face exchanges with few samples, which advances the development of this field. However, the images generated by previous GAN-based methods often show instability. The fundamental reason is that the GAN in these frameworks is difficult to converge to the distribution ofdoi:10.3389/fnbot.2021.785808 pmid:35126081 pmcid:PMC8814752 fatcat:ccenvadpsrconhkxoycjfvzyhq