通过反学习合成图像实现文本到图像模型的数据归因
Data Attribution for Text-to-Image Models by Unlearning Synthesized Images
AI总结：
针对文本到图像模型的数据归因需反复重训练、成本过高的问题，本文提出模拟反学习合成图像并依据损失偏差识别影响训练样本的高效方法，且经金标准重训练验证优于已有方法。
摘要：
文本到图像模型的数据归因旨在找出对生成新图像影响最大的训练图像。影响的定义是：对于给定输出，如果模型在排除最具影响力图像后从头重新训练，将无法复现相同输出。遗憾的是，直接搜索这些有影响力的图像在计算上不可行，因为这需要反复从头重新训练模型。在本研究中，我们提出一种高效的数据归因方法，通过模拟反学习合成图像来实现。具体而言，我们提高输出图像上的训练损失，同时避免对其他无关概念造成灾难性遗忘。随后，我们识别反学习过程后损失出现显著偏差的训练图像，并将其标记为有影响力图像。我们使用计算成本高昂但属于“金标准”的从头重新训练来评估该方法，并证明其相较以往方法的优势。
英文摘要：
The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image. Influence is defined such that, for a given output, if a model is retrained from scratch without the most influential images, the model would fail to reproduce the same output. Unfortunately, directly searching for these influential images is computationally infeasible, since it would require repeatedly retraining models from scratch. In our work, we propose an efficient data attribution method by simulating unlearning the synthesized image. We achieve this by increasing the training loss on the output image, without catastrophic forgetting of other, unrelated concepts. We then identify training images with significant loss deviations after the unlearning process and label these as influential. We evaluate our method with a computationally intensive but "gold-standard" retraining from scratch and demonstrate our method's advantages over previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
