Nowadays, several GANs are available for face aging applications and this paper focuses on the insight comparison among the frequently used image-to-image translation GANs which are CycleGAN (Cycle-Consistent Adversarial Network) and AttentionGAN (Attention-Guided Generative Adversarial Network). Face aging can be beneficial in several domains such as in biometric systems for face recognition with age progression, in forensics for helping to find the missing children, in entertainment, and many more. In the face aging process, new face images are synthesized with the help of the input images and desired target images. One of its applications in the image-to-image transformation way is the face aging task. These methods tend to synthesize new data from input images that are highly realistic at the output. Recently, there is incredible progress in the arena of machine learning with generative adversarial network (GAN) methods.
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