Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Toward Efficient Data-Free Unlearning
Authors: Chenhao Zhang, Shaofei Shen, Weitong Chen, Miao Xu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed ISPF effectively tackles the challenge and outperforms existing methods. We evaluate the effectiveness of the ISPF which is composed of two proposed techniques, the Inhibited Synthesis (IS) and Post Filter (PF), on three widely used benchmark datasets, i.e., SVHN (Netzer et al. 2011), CIFAR-10 and CIFAR100 (Krizhevsky, Hinton et al. 2009). |
| Researcher Affiliation | Academia | Chenhao Zhang1, Shaofei Shen1, Weitong Chen2, Miao Xu1* 1University of Queensland 2University of Adelaide EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The overall algorithm is placed in Appendix B. |
| Open Source Code | Yes | Code https://github.com/Child Eden/ISPF |
| Open Datasets | Yes | We evaluate the effectiveness of the ISPF which is composed of two proposed techniques, the Inhibited Synthesis (IS) and Post Filter (PF), on three widely used benchmark datasets, i.e., SVHN (Netzer et al. 2011), CIFAR-10 and CIFAR100 (Krizhevsky, Hinton et al. 2009). |
| Dataset Splits | No | We test unlearned models on the forgetting test data Dtest f and the retaining test data Dtest r for obtaining accuracies Af and Ar, respectively. Implementation details are in the Appendix C. |
| Hardware Specification | No | No specific hardware details (like GPU models or CPU specifications) are provided in the main text. The paper mentions 'Implementation details are in the Appendix C.' but this appendix is not provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA) are provided in the main text. The paper mentions 'Implementation details are in the Appendix C.' but this appendix is not provided. |
| Experiment Setup | No | No specific hyperparameter values or detailed training configurations are provided in the main text. The paper mentions 'Implementation details are in the Appendix C.' but this appendix is not provided. |