02124nas a2200265   4500000000100000000000100001008004100002260001200043653003500055653001300090653001400103653001000117653001900127653001900146100001600165700001700181700002200198700001300220245009100233856008100324300001200405490000600417520142100423022001401844       2022                            d  c09/202210aGenerative Adversarial Network10aCycleGAN10aGated GAN10aPReLU10aSmooth L1 Loss10aStyle Transfer1 aRabia Tahir1 aKeyang Cheng1 aBilal Ahmed Memon1 aQing Liu00aA Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs  uhttps://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_12.pdf  a100-1080 v73 aThe applications of style transfer on real time photographs are very trending now. This is used in various applications especially in social networking sites such as SnapChat and beauty cameras. A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image. Besides, most of research work only focuses on some traditional painting style transfer on real photographs. However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model. In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images. Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation. Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates. Our loss function helps the proposed model in achieving better reconstructed images. The proposed model also occupies less memory space during training. We use various evaluation parameters to inspect the accuracy of our model. The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results.  a1989-1660