Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labelled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, to close the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to state-of-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 33 on unconditional ImageNet generation.
翻译:这些模型的主要缺点是需要贴标签的数据。在这项工作中,我们利用两种流行的不受监督的学习技术,即对抗训练和自我监督技术,以缩小有条件和无条件的GAN之间的差距。特别是,我们允许网络在代表学习任务上进行合作,同时对传统的GAN游戏持对立态度。自我监督的作用是鼓励歧视者学习在培训期间没有被遗忘的有意义的特征表现。我们用经验测试了学习过的图像表现质量和合成图像的质量。在同样的条件下,自我监督的GAN取得了与最先进的有条件的对应机构类似的业绩。最后,我们表明这种完全不受监督的学习方法可以扩大,在无条件的图像网络生成方面可以达到33分的FID。