This paper introduces conditional adversarial networks (cGANs) for image-to-image translation tasks. The proposed method uses a generative adversarial network (GAN) framework where the generator learns to map input images to corresponding output images, and the discriminator learns to distinguish between real and generated images. The key innovation is the use of conditional information in the GAN training process, allowing the model to learn a loss function that adapts to the specific task. This approach enables the same architecture to be applied to a wide variety of image translation problems, such as photo generation from label maps, edge map to photo translation, and image colorization. The method is evaluated on a variety of tasks and datasets, including semantic segmentation, architectural label to photo translation, and thermal to color photo translation. The results show that the proposed approach produces high-quality outputs that are visually realistic and comparable to existing methods. The paper also discusses the importance of using a structured loss function and the benefits of using a U-Net architecture for the generator, which allows for efficient information flow between layers. The method is also shown to be effective in handling large images and is able to produce results that are comparable to state-of-the-art methods. The paper concludes that conditional adversarial networks are a promising approach for many image-to-image translation tasks, especially those involving highly structured graphical outputs.This paper introduces conditional adversarial networks (cGANs) for image-to-image translation tasks. The proposed method uses a generative adversarial network (GAN) framework where the generator learns to map input images to corresponding output images, and the discriminator learns to distinguish between real and generated images. The key innovation is the use of conditional information in the GAN training process, allowing the model to learn a loss function that adapts to the specific task. This approach enables the same architecture to be applied to a wide variety of image translation problems, such as photo generation from label maps, edge map to photo translation, and image colorization. The method is evaluated on a variety of tasks and datasets, including semantic segmentation, architectural label to photo translation, and thermal to color photo translation. The results show that the proposed approach produces high-quality outputs that are visually realistic and comparable to existing methods. The paper also discusses the importance of using a structured loss function and the benefits of using a U-Net architecture for the generator, which allows for efficient information flow between layers. The method is also shown to be effective in handling large images and is able to produce results that are comparable to state-of-the-art methods. The paper concludes that conditional adversarial networks are a promising approach for many image-to-image translation tasks, especially those involving highly structured graphical outputs.