Sijia Liu, Dennis Wei, Eric Wong, Yihua Zhang, Jiancheng Liu, Chongyu Fan
We lifted 11 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| optml-group/unlearn-saliency | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| FGSM_perturb | Ran | optml-group/unlearn-saliency/Classification/unlearn/boundary_sh.py code served (permissive licence) · get_code("074c8fc7870f5cc9") |
| discretize | Ran | optml-group/unlearn-saliency/Classification/unlearn/boundary_sh.py code served (permissive licence) · get_code("20b8d1668895f8bb") |
| fisher | Ran | optml-group/unlearn-saliency/Classification/unlearn/fisher.py code served (permissive licence) · get_code("3d980bf223d0e00b") |
| fisher_information_martix | Ran | optml-group/unlearn-saliency/Classification/unlearn/fisher.py code served (permissive licence) · get_code("c25079d6cf22a5e2") |
| get_require_grad_params | Ran | optml-group/unlearn-saliency/Classification/unlearn/Wfisher.py code served (permissive licence) · get_code("aebe5de64c1725b4") |
| l1_regularization | Ran | optml-group/unlearn-saliency/Classification/unlearn/FT.py code served (permissive licence) · get_code("cae29c9fba744465") |
| sam_grad | Ran | optml-group/unlearn-saliency/Classification/unlearn/Wfisher.py code served (permissive licence) · get_code("4145035921358f67") |
| woodfisher | Ran | optml-group/unlearn-saliency/Classification/unlearn/Wfisher.py code served (permissive licence) · get_code("6fdded37bf329957") |
| GA | Not yet run | optml-group/unlearn-saliency/Classification/unlearn/GA_prune.py code served (permissive licence) · get_code("1e7882ed2e1e9d82") |
| GA | Not yet run | optml-group/unlearn-saliency/Classification/unlearn/GA_prune_bi.py code served (permissive licence) · get_code("552196cf57918ac6") |
| get_mean_var | Not yet run | optml-group/unlearn-saliency/Classification/unlearn/fisher.py code served (permissive licence) · get_code("e953ece8832f74dd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often suffer limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' for MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting data points). To the best of our knowledge, SalUn is the first principled MU approach that can effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation tasks. For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-ofthe-art baselines like Erased Stable Diffusion and Forget-Me-Not.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.12508")
get_code_for_paper("2310.12508")
have("2310.12508")
Connect an agent — have() is free.