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This paper presents a thorough investigation into the capabilities of Cycle GANs for image-to-image translation tasks, particularly in scenarios where paired training data is unavailable.Unlike conventional GANs, Cycle GANs excel in learning mappings between two domains, A and B, without the necessity of paired samples from both domains.Through the enforcement of cycle-consistency, Cycle GANs ensure robustness in translating images from domain A to domain B and vice versa.This study provides an in-depth analysis of the architecture, implementation strategies, and diverse applications of Cycle GANs across fields such as art, medicine, and autonomous driving.
Khan et al. (Tue,) studied this question.