DMin: Scalable Training Data Influence Estimation for Diffusion Models

Autor:	Lin, Huawei, Lao, Yingjie, Zhao, Weijie
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models, yet existing influence estimation methods are constrained to small-scale or LoRA-tuned models due to computational limitations. As diffusion models scale up, these methods become impractical. To address this challenge, we propose DMin (Diffusion Model influence), a scalable framework for estimating the influence of each training data sample on a given generated image. By leveraging efficient gradient compression and retrieval techniques, DMin reduces storage requirements from 339.39 TB to only 726 MB and retrieves the top-k most influential training samples in under 1 second, all while maintaining performance. Our empirical results demonstrate DMin is both effective in identifying influential training samples and efficient in terms of computational and storage requirements. Comment: 14 pages, 6 figures, 8 tables. Under Review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.08637 Zobrazit plný text záznamu View this record from Arxiv