用于无监督图像生成与分割的分解扩散架构
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation
AI总结：
提出一种内置计算瓶颈的分解去噪扩散架构，仅依靠扩散目标即可无监督地同时完成图像生成与语义分割，并可零微调迁移到真实图像分割。
摘要：
我们开发了一种神经网络架构，该架构以去噪扩散模型的形式进行无监督训练，同时学习生成和分割图像。学习完全由去噪扩散目标驱动，训练期间没有任何标注或关于区域的先验知识。内置于神经架构中的计算瓶颈促使去噪网络将输入划分为多个区域，并行去噪这些区域，并组合结果。我们训练后的模型既能生成合成图像，又能通过简单检查其内部预测的分区来生成这些图像的语义分割。无需任何微调，我们通过对真实图像进行加噪并随后去噪，将无监督模型直接应用于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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
