DualGAN is an unsupervised dual learning framework for image-to-image translation. It enables image translators to be trained using two sets of unlabeled images from two domains. The primal GAN learns to translate images from domain U to domain V, while the dual GAN learns to invert the task. The closed loop formed by the primal and dual tasks allows images from either domain to be translated and then reconstructed, enabling the use of a loss function that accounts for reconstruction error to train the translators. Experiments on multiple image translation tasks with unlabeled data show that DualGAN outperforms single GANs and can achieve results comparable to or better than conditional GANs trained on labeled data. DualGAN is inspired by dual learning in natural language processing, where two "opposite" language translators are trained simultaneously by minimizing reconstruction loss. In image-to-image translation, DualGAN uses GAN discriminators to capture domain distributions without relying on pre-trained language models. The framework employs FCNs as translators, which naturally accommodate the 2D structure of images. The method involves two GANs: the primal GAN and the dual GAN. The primal GAN learns to translate images from domain U to domain V, while the dual GAN learns to translate images from domain V to domain U. The two GANs are trained simultaneously, with the primal GAN learning to generate realistic images and the dual GAN learning to reconstruct images from the generated outputs. The loss function used in DualGAN includes a reconstruction loss that measures the difference between the reconstructed and original images, as well as a discriminator loss that measures the difference between real and generated images. DualGAN has been tested on various image translation tasks, including photo-sketch conversion, label-image translation, and artistic stylization. The results show that DualGAN produces sharper and more accurate translations compared to GANs and cGANs, especially in tasks where labeled data is not available. The framework is able to generate visually convincing images even when no corresponding images are available in the target domain. The effectiveness of DualGAN is validated through comparison with GANs and cGANs, showing that it can outperform supervised methods trained on labeled data in several tasks. The method is also compared with other unsupervised image-to-image translation methods, such as CycleGAN and CoGAN. While CycleGAN and CoGAN also aim for general-purpose image-to-image translation, DualGAN's dual learning approach allows for better reconstruction and more accurate translations. The results show that DualGAN outperforms CycleGAN and CoGAN in several tasks, particularly in tasks where labeled data is not available. The framework is able to generate high-quality translations without relying on labeled data, making it a promising approach for image-to-image translation.DualGAN is an unsupervised dual learning framework for image-to-image translation. It enables image translators to be trained using two sets of unlabeled images from two domains. The primal GAN learns to translate images from domain U to domain V, while the dual GAN learns to invert the task. The closed loop formed by the primal and dual tasks allows images from either domain to be translated and then reconstructed, enabling the use of a loss function that accounts for reconstruction error to train the translators. Experiments on multiple image translation tasks with unlabeled data show that DualGAN outperforms single GANs and can achieve results comparable to or better than conditional GANs trained on labeled data. DualGAN is inspired by dual learning in natural language processing, where two "opposite" language translators are trained simultaneously by minimizing reconstruction loss. In image-to-image translation, DualGAN uses GAN discriminators to capture domain distributions without relying on pre-trained language models. The framework employs FCNs as translators, which naturally accommodate the 2D structure of images. The method involves two GANs: the primal GAN and the dual GAN. The primal GAN learns to translate images from domain U to domain V, while the dual GAN learns to translate images from domain V to domain U. The two GANs are trained simultaneously, with the primal GAN learning to generate realistic images and the dual GAN learning to reconstruct images from the generated outputs. The loss function used in DualGAN includes a reconstruction loss that measures the difference between the reconstructed and original images, as well as a discriminator loss that measures the difference between real and generated images. DualGAN has been tested on various image translation tasks, including photo-sketch conversion, label-image translation, and artistic stylization. The results show that DualGAN produces sharper and more accurate translations compared to GANs and cGANs, especially in tasks where labeled data is not available. The framework is able to generate visually convincing images even when no corresponding images are available in the target domain. The effectiveness of DualGAN is validated through comparison with GANs and cGANs, showing that it can outperform supervised methods trained on labeled data in several tasks. The method is also compared with other unsupervised image-to-image translation methods, such as CycleGAN and CoGAN. While CycleGAN and CoGAN also aim for general-purpose image-to-image translation, DualGAN's dual learning approach allows for better reconstruction and more accurate translations. The results show that DualGAN outperforms CycleGAN and CoGAN in several tasks, particularly in tasks where labeled data is not available. The framework is able to generate high-quality translations without relying on labeled data, making it a promising approach for image-to-image translation.