解密个性化：面向个性化图像生成模型的细粒度自然语言可解释性研究 (Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models)

Image generation models are usually personalized in practical uses in order to better meet the individual users' heterogeneous needs, but most personalized models lack explainability about how they are being personalized. Such explainability can be provided via visual features in generated images, but is difficult for human users to understand. Explainability in natural language is a better choice, but the existing approaches to explainability in natural language are limited to be coarse-grained. They are unable to precisely identify the multiple aspects of personalization, as well as the varying levels of personalization in each aspect. To address such limitation, in this paper we present a new technique, namely \textbf{FineXL}, towards \textbf{Fine}-grained e\textbf{X}plainability in natural \textbf{L}anguage for personalized image generation models. FineXL can provide natural language descriptions about each distinct aspect of personalization, along with quantitative scores indicating the level of each aspect of personalization. Experiment results show that FineXL can improve the accuracy of explainability by 56\%, when different personalization scenarios are applied to multiple types of image generation models.

翻译：图像生成模型在实际应用中通常需进行个性化处理，以更好地满足不同用户的异构需求，但多数个性化模型缺乏关于其如何实现个性化的可解释性说明。此类可解释性可通过生成图像中的视觉特征提供，但难以被人类用户理解。自然语言可解释性是更优选择，但现有自然语言可解释性方法仅限于粗粒度层面，无法精确识别个性化的多个维度及各维度中不同程度的个性化表现。为突破此局限，本文提出一种新技术——\textbf{FineXL}，旨在为个性化图像生成模型实现\textbf{细}粒度自然语言可\textbf{可解释性}。FineXL能够针对个性化的每个独立维度提供自然语言描述，并辅以量化分数表征各维度的个性化程度。实验结果表明，当将不同个性化场景应用于多种类型的图像生成模型时，FineXL可将可解释性准确率提升56%。