Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
Authors: Zhehao Huang, Xinwen Cheng, JingHao Zheng, Haoran Wang, Zhengbao He, Tao Li, Xiaolin Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate our efficacy and efficiency. Notably, our method successfully performs class-forgetting on Image Net using Di T and forgets a class on CIFAR-10 using DDPM in just 50 steps, compared to thousands of steps required by previous methods. |
| Researcher Affiliation | Academia | Zhehao Huang, Xinwen Cheng, Jing Hao Zheng, Haoran Wang, Zhengbao He, Tao Li, Xiaolin Huang Shanghai Jiao Tong University [kinght_H, xinwencheng, zjh20030406, haoran_whynot, lstefanie, li.tao, xiaolinhuang]@sjtu.edu.cn |
| Pseudocode | Yes | Appendix C Algorithm A1 The Algorithm of Proposed SFR-on |
| Open Source Code | Yes | Code is available at Unified-Unlearning-w-Remain-Geometry. |
| Open Datasets | Yes | In image classification, we primarily focus on the random subset unlearning task. Evaluations are conducted using Res Net-18 [52] on CIFAR10 [53] and Swin T [54] on Tiny Image Net [55], with additional tests on random subset and class-wise forgetting tasks involving CIFAR100 [53] and SVHN [56], detailed in Appendix F.2. ... Moreover, for the first time, we explore the latent diffusion model [42] equipped with Diffusion Transformer (Di T) [58] on Image Net [59]... Given that SD V1.4 is trained on the LAION dataset [63]... |
| Dataset Splits | No | The paper defines forgetting and remaining datasets (D_f, D_r) and mentions a test dataset (D_t), but does not explicitly provide information on a distinct validation dataset split or its proportions. |
| Hardware Specification | Yes | Experiments are run on 1 RTX 4090. ... Experiments are run on 2 RTX 4090s. |
| Software Dependencies | No | The paper mentions specific models and optimizers like 'Res Net-18', 'Swin T', 'DDPM', 'UNet', 'Di T', and 'Adam W optimizer', and refers to 'torchvision', but does not provide specific version numbers for these software components or programming languages. |
| Experiment Setup | Yes | Our SFR-on train 1500 steps with the constant outer loop learning rate of α = 1.0, inner loop iteration number Tin = 5. SFR-on search inner loop learning rate for forgetting in range [0.1, 0.5] and for remaining in range [10 3, 10 2], temperature scalar λ in range [0.0, 2.0], and threshold γ in list [0.3, 1.0, 3.0, 10.0]. Experiments are run on 1 RTX 4090. A summary of the hyperparameters for each method is shown in Tab. A1. |