Generator outputs of CycleGAN

Figure 7.1.9 shows the colorization results of CycleGAN. The source images are from the test dataset. For comparison, we show the ground truth and the colorization results using a plain autoencoder described in Chapter 3, Autoencoders. Generally, all colorized images are perceptually acceptable. Overall, it seems that each colorization technique has both its own pros and cons. All colorization methods are not consistent with the right color of the sky and vehicle.

For example, the sky in the background of the plane (3^rd row, 2^nd column) is white. The autoencoder got it right, but the CycleGAN thinks it is light brown or blue. For the 6^th row, 6^th column, the boat on the dark sea had an overcast sky but was colorized with blue sky and blue sea by autoencoder and blue sea and white sky by CycleGAN without PatchGAN. Both predictions make sense in the real world. Meanwhile, the prediction of CycleGAN with PatchGAN is similar to the ground truth. On 2^nd to...