[论文解读] Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet

Gonzalo Martínez, Lauren Watson+4 more|arXiv (Cornell University)|Jun 8, 2023

Computational Physics and Python Applications被引用 9

一句话总结

该论文分析了一个最坏情况的反馈回路：来自前几代的AI生成内容用于训练下一代扩散模型，显示在不同数据集上保真度和多样性可能退化。

ABSTRACT

The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already contributing to the data that is available to train future models. Therefore, future versions of generative AI tools will be trained with a mix of human-created and AI-generated content, causing a potential feedback loop between generative AI and public data repositories. This interaction raises many questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve and improve with the new data sets or on the contrary will they degrade? Will evolution introduce biases or reduce diversity in subsequent generations of generative AI tools? What are the societal implications of the possible degradation of these models? Can we mitigate the effects of this feedback loop? In this document, we explore the effect of this interaction and report some initial results using simple diffusion models trained with various image datasets. Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.

研究动机与目标

• 通过互联网数据生态系统来激发并形式化关于AI生成数据如何影响未来生成模型的问题。
• 探索通过生成式AI与训练数据之间的反馈回路可能导致的退化、偏见放大和多样性丧失。
• 在多个图像数据集上使用简单的扩散模型，在最坏互动模型下提供初步的经验洞见。
• 为更复杂的互动模型和更广泛的数据集奠定基础。

提出的方法

• 回顾用于图像生成的扩散模型及相关评估指标。
• 提出一个最坏-case的互动模型，在该模型中每个版本的模型都是在前一版本生成的数据上进行训练。
• 将扩散模型（两种变体：扩散隐式与无分类器引导）应用于MNIST、Oxford Flowers和Caltech-UCSD Birds数据集。
• 使用小图像（基于分类器的）指标和基于Inception的指标（FID、precision、density、recall、coverage）评估保真度与多样性。
• 在九代上进行实验，以观察随时间推移图像质量和多样性的趋势。

实验结果

研究问题

• RQ1 当在前一版本本身生成的数据上训练时，生成模型的保真度和多样性会发生什么变化？
• RQ2 AI生成的数据是否会在不同复杂度的数据集上导致图像质量的退化、稳定化或崩溃？
• RQ3 数据集复杂性和引导策略如何影响反馈回路中生成内容的演化？

主要发现

• 在训练数据被AI生成的数据主导时，某些数据集的保真度和多样性可能随时间退化。
• 引导强度对演化有强烈影响：高引导保持数字清晰度但降低多样性；极低引导会导致退化和可识别结构的丧失。
• 在复杂的彩色图像数据集（花朵、鸟类）上无引导场景会导致逐步退化并最终崩溃，诸如FID等指标在代数上呈上升趋势。
• 某些数据集显示先退化后稳定的动态，表明反馈回路具有数据集依赖性。