As synthetic data becomes higher quality and proliferates on the internet,
machine learning models are increasingly trained on a mix of human- and
machine-generated data. Despite the successful stories of using synthetic data
for representation learning, using synthetic data for generative model training
creates "self-consuming loops" which may lead to training instability or even
collapse, unless certain conditions are met. Our paper aims to stabilize
self-consuming generative model training. Our theoretical results demonstrate
that by introducing an idealized correction function, which maps a data point
to be more likely under the true data distribution, self-consuming loops can be
made exponentially more stable. We then propose self-correction functions,
which rely on expert knowledge (e.g. the laws of physics programmed in a
simulator), and aim to approximate the idealized corrector automatically and at
scale. We empirically validate the effectiveness of self-correcting
self-consuming loops on the challenging human motion synthesis task, and
observe that it successfully avoids model collapse, even when the ratio of
synthetic data to real data is as high as 100%.

通过引入理想化的校正函数，本文旨在稳定自消耗生成模型的训练，理论结果证明了根据真实数据分布将数据点映射为更可能的方法可以使自消耗循环的稳定性呈指数级增加，我们还提出了自我校正函数，它依赖于专家知识并旨在在规模上自动近似理想化的校正器，在具有挑战性的人体运动合成任务上对自我校正自消耗循环的有效性进行了实证验证，观察到即使合成数据与真实数据的比例高达 100%，也能成功避免模型崩溃。