We develop a neural network architecture which, trained in an unsupervised manner as a denoising diffusion model, simultaneously learns to both generate and segment images. Learning is driven entirely by the denoising diffusion objective, without any annotation or prior knowledge about regions during training. A computational bottleneck, built into the neural architecture, encourages the denoising network to partition an input into regions, denoise them in parallel, and combine the results. Our trained model generates both synthetic images and, by simple examination of its internal predicted partitions, a semantic segmentation of those images. Without any finetuning, we directly apply our unsupervised model to the downstream task of segmenting real images via noising and subsequently denoising them. Experiments demonstrate that our model achieves accurate unsupervised image segmentation and high-quality synthetic image generation across multiple datasets.

我们发展了一种神经网络架构，以无监督的方式作为去噪扩散模型进行训练，同时学习生成和分割图像。通过去噪扩散目标来推动学习，无需任何注释或有关区域的先验知识进行训练。神经网络的计算瓶颈鼓励去噪网络将输入分成区域，在并行处理中去噪并结合结果。我们训练的模型生成了合成图像，并通过简单检测其内部预测分割来进行语义分割。在没有任何微调的情况下，我们直接将我们的无监督模型应用于通过加噪和随后去噪来分割真实图像的下游任务。实验表明，我们的模型在多个数据集上实现了准确的无监督图像分割和高质量的合成图像生成。

因式分解扩散结构用于无监督图像生成与分割