평범한 필기장

Paper : https://arxiv.org/abs/2411.00113 A Geometric Framework for Understanding Memorization in Generative ModelsAs deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the legal andarxiv.orgAbstract본 논문은 memori..

Paper : https://arxiv.org/abs/2411.16738 Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationDiffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement and/or leakage of privacy-sensitive information. In this paper, we present aarxiv.orgAbstract해결하려는 문제 D..

Paper : https://arxiv.org/abs/2407.21720 Detecting, Explaining, and Mitigating Memorization in Diffusion ModelsRecent breakthroughs in diffusion models have exhibited exceptional image-generation capabilities. However, studies show that some outputs are merely replications of training data. Such replications present potential legal challenges for model owners, espearxiv.orgAbstract문제 : 생성모델의 몇 o..