L1 loss problem with GAN if some data has ground-truth and some not about gan HOT 2 OPEN

Generally the way you would do something like this in Tensorflow is to have some dummy value for the ground truth when it's not available (like an all 0's tensor, for example) and keep a binary mask of shape [batch_size] that has value 1 if there is ground truth and 0 otherwise. You can then use this as a weight applied to the per-sample loss. Your final loss would be something like:
loss = tf.reduce_mean(weight_mask * tf.losses.mean_absolute_error(ground_truth, generated)

from gan.

Hi @HymEric
Maybe you could try using an external function such that g(ground-truth) --> generated, before using L1 loss, as mentioned in junyanz/pytorch-CycleGAN-and-pix2pix#293 (comment)

from gan.