论文标题
改进的基于培训得分生成模型的技术
Improved Techniques for Training Score-Based Generative Models
论文作者
论文摘要
基于得分的生成模型可以产生与gan相当的高质量图像样品，而无需对抗性优化。但是，现有的培训程序仅限于低分辨率的图像（通常低于32x32），并且在某些设置下可能不稳定。我们从高维空间中的分数模型中提供了对学习和采样的新理论分析，解释了现有的故障模式并激发了跨数据集概括的新解决方案。为了提高稳定性，我们还建议保持模型权重的指数移动平均值。通过这些改进，我们可以毫不费力地基于得分的生成模型，以范围为64x64至256x256的图像。我们基于分数的模型可以生成高保真样本，这些样本可与各种图像数据集中的一流甘斯抗衡，包括Celeba，FFHQ和多个LSUN类别。
Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining existing failure modes and motivating new solutions that generalize across datasets. To enhance stability, we also propose to maintain an exponential moving average of model weights. With these improvements, we can effortlessly scale score-based generative models to images with unprecedented resolutions ranging from 64x64 to 256x256. Our score-based models can generate high-fidelity samples that rival best-in-class GANs on various image datasets, including CelebA, FFHQ, and multiple LSUN categories.
