Cycle Consistent Adversarial Network

Cycle-consistent adversarial networks (CycleGANs) are a type of generative adversarial network used for unpaired image-to-image translation, aiming to learn mappings between two image domains without requiring corresponding paired examples. Current research focuses on applying CycleGANs and its variants to diverse problems, including image enhancement (e.g., thermal image calibration, artifact reduction in medical imaging), data augmentation (e.g., synthetic MRI generation from CT scans, code-switching text generation), and anomaly detection in various domains. This approach offers a powerful tool for addressing data scarcity and domain adaptation challenges across numerous scientific fields and practical applications, improving the performance of downstream tasks such as medical diagnosis and precipitation forecasting.