Truly impressive overall. Unfortunately, it looks like training set was way too small. Look for example at reconstruction of #13 here: https://phillipi.github.

Truly impressive overall. Unfortunately, it looks like training set was way too small. Look for example at reconstruction of #13 here: https://phillipi.github.io/pix2pix/images/index_facades2_loss_variations.html https://phillipi.github.io/pix2pix/images/index_facades2_los... Notice white triangles (image crop artifacts) present on the original image, yet completely absent on the net input image. They make re-appearance on the output of 3 (4 even?) out of 5 nets despite the lack of corresponding cue in the input image. Looks like network cheated a bit here, i.e. took advantage of small set size and memorized the input image as a whole. Then recognized and recalled this very image (already seen during training) rather than actually reconstructing it purely from the input. Same (but less prominent) for other images where "ground truth" image was cropped.