改进生成模型自训练：几何修正输出

中文摘要 AI 辅助

自训练生成模型——即利用模型自身输出持续改进模型——随着高质量训练数据的日益稀缺而变得愈发重要。然而，天真地在模型生成的样本上进行微调会导致模型坍缩和模型自噬障碍，从而引发性能退化。负引导自训练方法将这种退化转化为有用的信号，利用在自身输出上微调的模型来引导原始模型朝向更优的生成结果。然而，现有方法将标准模型输出中的负信号视为既定事实。我们转而探究这一信号能否被显式增强。我们引入了几何修正输出（GMOs），该方法对生成器的输入-输出雅可比矩阵的奇异值进行重新加权，以增强其主导奇异方向的影响。这种几何修正放大了标准输出的模式寻求行为和失真，为自训练提供了更强、更具针对性的负信号。在一系列单步生成模型上，与使用标准模型输出相比，GMOs持续提升了负引导方法（包括Neon和SIMS）的性能。

英文摘要

Self-training generative models - the continued improvement of a model using its own outputs - is becoming increasingly important as high-quality training data becomes scarce. However, naively finetuning on model-generated samples leads to degradation through model collapse and the model autophagy disorder. Negative-guidance self-training methods turn this degradation into a useful signal, using a model finetuned on its own outputs to guide the original model toward improved generation. Existing methods, however, take the negative signal in standard model outputs as given. We instead ask whether this signal can be explicitly strengthened. We introduce Geometrically Modified Outputs (GMOs), which reweight the singular values of the generator's input-output Jacobian to increase the influence of its leading singular directions. This geometric modification amplifies the mode-seeking behavior and distortions of standard outputs, providing a stronger and more targeted negative signal for self-training. Across a range of one-step generative models, GMOs consistently improve the performance of negative-guidance methods, including Neon and SIMS, compared with using standard model outputs.

发表机构

• Rice University（莱斯大学）

机构由 AI 辅助整理，请以论文原文为准。