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。

用于生成模型训练的自校正自消耗循环

Self-Correcting Self-Consuming Loops for Generative Model Training

Predict+Optimize is a recently proposed framework which combines machine
learning and constrained optimization, tackling optimization problems that
contain parameters that are unknown at solving time. The goal is to predict the
unknown parameters and use the estimates to solve for an estimated optimal
solution to the optimization problem. However, all prior works have focused on
the case where unknown parameters appear only in the optimization objective and
not the constraints, for the simple reason that if the constraints were not
known exactly, the estimated optimal solution might not even be feasible under
the true parameters. The contributions of this paper are two-fold. First, we
propose a novel and practically relevant framework for the Predict+Optimize
setting, but with unknown parameters in both the objective and the constraints.
We introduce the notion of a correction function, and an additional penalty
term in the loss function, modelling practical scenarios where an estimated
optimal solution can be modified into a feasible solution after the true
parameters are revealed, but at an additional cost. Second, we propose a
corresponding algorithmic approach for our framework, which handles all packing
and covering linear programs. Our approach is inspired by the prior work of
Mandi and Guns, though with crucial modifications and re-derivations for our
very different setting. Experimentation demonstrates the superior empirical
performance of our method over classical approaches.

本文提出了一个新的 Predict+Optimize 框架，可以处理既有在优化目标又有在约束条件中的未知参数，引入了校正函数和惩罚项来解决相关问题，同时提出了一种可处理所有 packing 和 covering 线性方程的算法，实验证明该方法比传统方法表现优秀。