Mode collapse in GANs occurs when the generator outputs less diverse data than the real-world samples it imitates, impacting the authenticity of AI-generated content.
DynGAN, a new framework developed by researchers, actively tackles mode collapse by monitoring and adjusting the diversity of generated data, leading to more realistic outputs compared to traditional GAN models.
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