Fig. 1.

Our network architecture. The generator is based on a U-Net architecture consisting of residual blocks with adaptive instance normalization. The input labels ($y_i$) are injected into the encoder part of the generator, and the target labels ($y_t$) are injected into the decoder part of the generator. The generator is trained on the reconstruction, adversarial, and conditioning loss