Disentangled Image Attribute Editing in Latent Space via Mask-Based Retention Loss

We propose an image attribute editing method with the mask-based retention loss. Although conventional image attribute editing methods can edit a particular attribute, they cannot retain non-editing attributes including unknown attributes before and after editing, which causes unexpected changes in the edited images. We solve this problem by dividing the pre- and post-edited images into the editing and non-editing regions and increasing the image similarity in the non-editing regions. In this paper, we introduce the novel mask-based retention loss to retain the non-editing regions. To compute the mask-based retention loss, we divide the images into the editing and non-editing regions by using a binary mask generated from the difference between the pre- and post-edited images. Experimental results show that our proposed method is qualitatively and quantitatively superior to state-of-the-art methods.