English(EN) Improving Generative Model Self-Training with Geometrically Modified Outputs

新技术通过放大输出失真来增强生成模型的自训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-28 16:07

研究人员开发了一种名为几何修改输出（GMOs）的新技术，以改进生成模型的自训练。该方法解决了模型退化问题，也称为模型崩溃或自噬障碍，当模型在自身输出来进行持续训练时会发生。通过重新加权生成器输入-输出雅可比行列式的奇异值，GMOs放大了模型的固有偏差和失真，产生了更强的负信号。这种放大的信号增强了Neon和SIMS等现有负引导自训练方法的有效性，与使用标准模型输出相比，性能得到了提升。 AI

影响这项技术可能导致更有效和高效的生成模型训练，尤其是在高质量数据稀缺的情况下。

排序理由该集群包含一篇详细介绍改进生成模型自训练的新技术的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-09-28 16:07

使用几何修改的输出来改进生成模型自训练

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 t…

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使用几何修改的输出来改进生成模型自训练

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