WeaGAN:Generative Adversarial Network for Weather Translation of Image among Multidomain Weather translation of image refers to the task of changing the weather of an input image to desired weather while preserving the structure of the imageu0027s content, which belongs to a task of imagetoimage translation. Recent works have made great process in imagetoimage translation between two domains and some works have even achieved multidomain translation within a single model. However, existing works have limited robustness in handling weather translation among multidomain, since bad weather produces a loud noise and it is challenging to process scene images without fixed pattern in a unified model. In this paper, we propose WeaGAN based on encoderdecoder architecture and generative adversarial training process to translate the weather of image among multidomain. In particular, We employ SE block in generator and combine adversarial loss, classification loss and content loss for visually detailed and realistic result. Experience in qualitative and quantitative aspect on synthetic dataset and real dataset show the effectiveness and competitiveness of our method compared with stateoftheart works.

GANILLA: Generative adversarial networks for image to illustration translation Abstract   In this paper, we explore illustrations in childrenu0027s books as a new domain in unpaired imagetoimage translation. We show that although the current stateoftheart imagetoimage translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content.  There are no welldefined or agreedupon evaluation metrics for unpaired imagetoimage translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of imagetoillustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current stateoftheart models on the illustrations dataset. Our code and pretrained models can be found at  https:github.comgiddyyuppganilla .