Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation

Understand

Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial networks (GANs).

• However, due to the lack of explicit supervision, unpaired I2I models often fail to generate realistic images, especially in challenging datasets with different backgrounds and poses.
• Hence, stabilization is indispensable for GANs and applications of I2I translation.
• Herein, we propose Augmented Cyclic Consistency Regularization (ACCR), a novel regularization method for unpaired I2I translation.