Controlling biases and diversity in diverse image-to-image translation

Understand

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances.

• We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations.
• This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content.
• Despite the success of current DIT models, they are prone to suffer from bias.