This paper presents a novel loss-sensitive generative adversarial net (LS-GAN). Compared with the classic GAN that uses a dyadic classification of real and generated samples to train the discriminator, we learn a loss function that can generate samples with the constraint that a real example should have a smaller loss than a generated sample. This results in