Non-autoregressive generative transformers recently demonstrated impressive
image generation performance, and orders of magnitude faster sampling than
their autoregressive counterparts. However, optimal parallel sampling from the
true joint distribution of visual tokens remains an open challenge. In this
paper we introduce Token-Critic, an auxiliary model to guide the sampling of a
non-autoregressive generative transformer. Given a masked-and-reconstructed
real image, the Token-Critic model is trained to distinguish which visual
tokens belong to the original image and which were sampled by the generative
transformer. During non-autoregressive iterative sampling, Token-Critic is used
to select which tokens to accept and which to reject and resample. Coupled with
Token-Critic, a state-of-the-art generative transformer significantly improves
its performance, and outperforms recent diffusion models and GANs in terms of
the trade-off between generated image quality and diversity, in the challenging
class-conditional ImageNet generation.

该论文介绍了 Token-Critic，一个辅助模型，用于指导非自回归生成变压器的采样，该模型可选择要接受和要拒绝和重新采样的 token，结合 Token-Critic，生成变压器在 ImageNet 生成方面优于最近的扩散模型和 GANs，可以在生成图像质量和多样性之间取得良好的平衡。

使用 Token-Critic 改进的遮蔽图像生成

Improved Masked Image Generation with Token-Critic

Conditional GANs are at the forefront of natural image synthesis. The main
drawback of such models is the necessity for labeled data. In this work we
exploit two popular unsupervised learning techniques, adversarial training and
self-supervision, and take a step towards bridging 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 23.4 on
unconditional ImageNet generation.

利用对抗性训练和自监督技巧，通过任务的表示学习促进判别器学习有意义的特征表示的全无监督学习方法产生高质量的图像。在相同的条件下，自监督生成对抗网络的表现与有标记数据的生成网络相似。同时，我们还演示了该方法可扩展到一个无条件的图像集中，以实现 FID 为 23.4.