Mitigating Exposure Bias in Discriminator Guided Diffusion Models
Eleftherios Tsonis Paraskevi Tzouveli Athanasios Voulodimos
Diffusion Models have demonstrated remarkable performance in image
generation. However, their demanding computational requirements for training
have prompted ongoing efforts to enhance the quality of generated images
through modifications in the sampling process. A recent approach, known as
Discriminator Guidance, seeks to bridge the gap between the model score and the
data score by incorporating an auxiliary term, derived from a discriminator
network. We show that despite significantly improving sample quality, this
technique has not resolved the persistent issue of Exposure Bias and we propose
SEDM-G++, which incorporates a modified sampling approach, combining
Discriminator Guidance and Epsilon Scaling. Our proposed approach outperforms
the current state-of-the-art, by achieving an FID score of 1.73 on the
unconditional CIFAR-10 dataset.