GANs	Pros/Cons
CycleGAN	Pros:
	CycleGAN’s biggest advantage is that it can produce remarkable results without the need for a paired dataset. Moreover, it works well for texture and color changes [33].
	For identity preservation (Table 2), FID score (Table 3, figure 15(b)) and SSIM (figures 15(a)-(d)) CycleGAN is a better performer than AttentionGAN in this paper.
	Cons:
	In this paper, the training time required by CycleGAN is more for the same number of epochs and training images. Because this paper focuses on face age progression only, CycleGAN performs bidirectional translation simultaneously (converting the images for progression as well as for regression process.)
AttentionGAN	Pros:
	AttentionGAN works for the unpaired dataset.
	As illustrated in figure 11, individual results synthesized by AttentionGAN are better than CycleGAN. Individually, it has produced more realistic and sharper aged face images.
	Cons:
	AttentionGAN produces artifacts in some images. CycleGAN images are better than AttentionGAN (figure 10).