 Evaluation results  GANs are notoriously difficult to train, with the losses for the Discriminator and the Generator often failing to converge even when producing what looks to be a highly realistic result to a human eye. The pixelwise loss for the test images also serves as a poor indicator of the model's performance because any variation in the lighting between the real target photo and the generated image could result in a deceptively high discrepancy between the two. The best evaluation method that remains is the manual inspection of the generated results. We have found that the present model performs reasonably well on the test data from the CMU Multi-PIE Face Database (naturally, all of the photos of the individuals included in the test set were removed from training):  ![test examples](https://github.com/scaleway/frontalization/raw/master/pretrained/test-Pie.jpg)  (Top row: inputs; middle row: model outputs; bottom row: ground truth images)