Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
Authors: Huiqiang Chen, Tianqing Zhu, Xin Yu, Wanlei Zhou
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment 4.1 Experimental Setup Dataset and model architecture. Following previous works, we evaluate the proposed method on four benchmarks: Facescrub [Ng and Winkler, 2014], SVHN [Netzer et al., 2011], CIFAR-10 and CIFAR-100 [Krizhevsky et al., 2009]. We apply four representative network architectures in our experiments: Alex Net [Krizhevsky et al., 2012], VGG [Simonyan and Zisserman, 2014], Res Net [He et al., 2015], and Vi T [Dosovitskiy et al., 2020]. Baselines. We compare our approach with several baselines. |
| Researcher Affiliation | Academia | Huiqiang Chen1,2 , Tianqing Zhu1 , Xin Yu3 , Wanlei Zhou1 1City University of Macau, Macau, China 2University of Technology Sydney, NSW, Australia 3University of Queensland, QLD, Australia EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed method in Section 3, detailing the steps for proxy adversarial data generation, unlearning with orthogonal projection, and influence-based pseudo-label optimization using mathematical formulations and descriptive text, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, a link to a code repository, or information indicating code availability in supplementary materials. |
| Open Datasets | Yes | Dataset and model architecture. Following previous works, we evaluate the proposed method on four benchmarks: Facescrub [Ng and Winkler, 2014], SVHN [Netzer et al., 2011], CIFAR-10 and CIFAR-100 [Krizhevsky et al., 2009]. |
| Dataset Splits | Yes | Dataset and model architecture. Following previous works, we evaluate the proposed method on four benchmarks: Facescrub [Ng and Winkler, 2014], SVHN [Netzer et al., 2011], CIFAR-10 and CIFAR-100 [Krizhevsky et al., 2009]. ... Evaluation metrics and implement details. Following the literature, we assess the unlearned model with three metrics: 1) Accut: Accuracy on the testing set of unlearning classes. ... 3) Accrt: Accuracy on testing set of remaining classes. |
| Hardware Specification | Yes | Computational cost of ZS-PAG. We conduct the experiment on an NVIDIA RTX 4090 GPU. |
| Software Dependencies | No | The paper does not explicitly list any specific software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | We utilize projected gradient descent [Madry et al., 2017] to generate adversary samples Dadv in the experiment. ... We fix the unlearning epochs to 10 for a fair comparison. ... As shown in the experiment, setting nadv = 100 is sufficient for our needs. ... We generate adversarial samples with varying noise bound ε. |