Figure 5.

Schematic diagram of CycleEBGAN. Real and cycled fundus photographs were converted into angiographs by G and F, respectively. Real and cycled angiographs were converted into simulated fundus photographs by F and G, respectively. The discriminators DX and DY were trained to distinguish between the real and simulated images. The generators were trained to “fool” the discriminator to make the simulated image look like a real image. We calculated the L1 loss ${ℒ}_{\text{cyc}}\left(G, F\right)$ so that the real and cycled images were identical. In addition, in CycleEBGAN, L2 loss ${ℒ}_{\text{energy}}\left(\text{G, } \text{ F, } \text{ }{D}_Y\text{,}{D}_X\right)$ led to the constraint of identical real and simulated images (red arrows). The main difference between CycleEBGAN and CycleGAN is that CycleGAN measures the distance between the simulated image and 1, whereas CycleEBGAN measures the distance between the simulated and paired real images (red arrows). CycleEBGAN = energy-based cycle-consistent adversarial networks, CycleGAN = cycle-consistent adversarial networks.