通过ε约束优化实现图像到图像生成模型的可控遗忘
Controllable Unlearning for Image-to-Image Generative Models via $\varepsilon$-Constrained Optimization
AI总结：
针对图像到图像生成模型机器遗忘中用户对完全遗忘与模型效用权衡需求多样化的问题，提出基于ε约束优化的可控遗忘框架，通过控制系数调控权衡并保证解的帕累托最优，实验验证了其有效性。
摘要：
近年来生成模型虽取得显著进展，但也引发隐私泄露和偏见等问题。机器遗忘作为可行解决方案，旨在从模型中移除含隐私信息或偏见等特定训练数据。本文研究图像到图像（I2I）生成模型的机器遗忘问题。现有研究多将其视为单目标优化问题，仅提供单一解，忽视用户对完全遗忘与模型效用权衡的多样化需求。为此，我们提出可控遗忘框架，通过控制系数ε调控该权衡。将I2I生成模型遗忘问题重构为ε约束优化问题，用基于梯度的方法求解最优遗忘边界，该边界定义控制系数的有效范围，范围内每个解理论上保证帕累托最优。我们还分析了框架在不同控制函数下的收敛速率。在两个基准数据集上对三种主流I2I模型开展的大量实验，验证了可控遗忘框架的有效性。
英文摘要：
While generative models have made significant advancements in recent years, they also raise concerns such as privacy breaches and biases. Machine unlearning has emerged as a viable solution, aiming to remove specific training data, e.g., containing private information and bias, from models. In this paper, we study the machine unlearning problem in Image-to-Image (I2I) generative models. Previous studies mainly treat it as a single objective optimization problem, offering a solitary solution, thereby neglecting the varied user expectations towards the trade-off between complete unlearning and model utility. To address this issue, we propose a controllable unlearning framework that uses a control coefficient $\varepsilon$ to control the trade-off. We reformulate the I2I generative model unlearning problem into a $\varepsilon$-constrained optimization problem and solve it with a gradient-based method to find optimal solutions for unlearning boundaries. These boundaries define the valid range for the control coefficient. Within this range, every yielded solution is theoretically guaranteed with Pareto optimality. We also analyze the convergence rate of our framework under various control functions. Extensive experiments on two benchmark datasets across three mainstream I2I models demonstrate the effectiveness of our controllable unlearning framework.
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。
