From 40ef4bb274746292954f48b486a1fb12539dd55c Mon Sep 17 00:00:00 2001 From: ljcc Date: Fri, 15 Mar 2024 21:00:42 -0400 Subject: [PATCH] Initialize project page --- _data/publications/conferences.yml | 4 +- images/postpic/salun_iclr24/teaser-v2.png | Bin 0 -> 164796 bytes posts/Sparse_unlearn_neurips23.md | 2 +- posts/salun_iclr24.md | 54 ++++++++++++++++++++++ 4 files changed, 57 insertions(+), 3 deletions(-) create mode 100644 images/postpic/salun_iclr24/teaser-v2.png create mode 100644 posts/salun_iclr24.md diff --git a/_data/publications/conferences.yml b/_data/publications/conferences.yml index 2852f71..5e255b9 100644 --- a/_data/publications/conferences.yml +++ b/_data/publications/conferences.yml @@ -5,9 +5,9 @@ link: url: https://arxiv.org/abs/2310.12508 display: ICLR’24 (Spotlight, acceptance rate 5%) - highlight: 0 + highlight: 1 news2: - post: + post: salun_iclr24 - title: "DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training" image: deepzero_iclr24.png 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"Jinghan Jia[1]*, Jiancheng Liu[1]*, diff --git a/posts/salun_iclr24.md b/posts/salun_iclr24.md new file mode 100644 index 0000000..4bd728c --- /dev/null +++ b/posts/salun_iclr24.md @@ -0,0 +1,54 @@ +--- +layout: paper +title: "[ICLR24 Spotlight] SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" +date: 2024-03-15 21:00:00 +author: "Chongyu Fan[1]*, + Jiancheng Liu[1]*, + Yihua Zhang[1], + Eric Wong[2], + Dennis Wei[3], + Sijia Liu[1,3]" +affiliation: "[1]Michigan State University, [2]University of Pennsylvania, [3]IBM Research" +code: "https://github.com/OPTML-Group/Unlearn-Saliency" +# poster: "" +paper: "https://arxiv.org/pdf/2310.12508.pdf" +--- +

+ +
+
+ Figure 1: Schematic overview of our proposal on Saliency Unlearning (SalUn).
+
+ +--- + +## Abstract + + +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. As highlighted below, 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-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not. Codes are available at this https URL. + +**WARNING**: This paper contains model outputs that may be offensive in nature. + +--- + + +## Acknowledgement + +C. Fan, J. Liu, and S. Liu were supported by the Cisco Research Faculty Award and the National Science Foundation (NSF) Robust Intelligence (RI) Core Program Award IIS-2207052. + +--- + +## Citation +``` +@article{fan2023salun, + title={Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation}, + author={Fan, Chongyu and Liu, Jiancheng and Zhang, Yihua and Wei, Dennis and Wong, Eric and Liu, Sijia}, + journal={arXiv preprint arXiv:2310.12508}, + year={2023} +} +``` \ No newline at end of file