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# SCAR: Selective-distillation for Class and Architecture-agnostic unleaRning | ||
[Jacopo Bonato](https://scholar.google.com/citations?user=tC1GFkUAAAAJ&hl=it&authuser=1),[Marco Cotogni](https://scholar.google.com/citations?user=8PUz5lAAAAAJ&hl=it), [Luigi Sabetta](https://scholar.google.com/citations?view_op=list_works&hl=en&user=rQBQQjMAAAAJ) | ||
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<!--The paper is available on [![arxiv](https://img.shields.io/badge/arXiv-red)]()--> | ||
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## Overview | ||
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SCAR is a novel model-agnostic unlearning algorithm named Selective-distillation for Class and Architecture-agnostic unleaRning. SCAR utilizes metric learning and knowledge distillation techniques to efficiently remove targeted information from models without relying on a retain set. By leveraging the Mahalanobis distance, SCAR shifts feature vectors of instances to forget towards distributions of samples from other classes, facilitating effective metric learning-based unlearning. Additionally, SCAR maintains model accuracy by distilling knowledge from the original model using out-of-distribution images. | ||
![Time](imgs/fig1.png) | ||
Key contributions of this work include the development of SCAR, which achieves competitive unlearning performance without retain data, a unique self-forget mechanism in class removal scenarios, comprehensive analyses demonstrating efficacy across different datasets and architectures, and experimental evidence showcasing SCAR's comparable or superior performance to traditional unlearning methods and state-of-the-art techniques that do not use a retain set. | ||
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## Getting Started | ||
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### Installation | ||
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```bash | ||
# Clone the repository | ||
git https://github.com/jbonato1/SCAR | ||
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# Navigate to the project directory | ||
cd your-repo | ||
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# Installation WITH DOCKER | ||
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#Step 1: | ||
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#Build the docker image from the Dockerfile : | ||
docker build -f Dockerfile -t scar:1.0 . | ||
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#Step 2: | ||
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#Run your image : | ||
docker run -it --gpus all -v "/path_to_dataset_folder":/root/data -v "/path_to_scar_folder":/scar scar:1.0 /bin/bash | ||
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# Install LOCALLY | ||
pip install -r requirements.txt | ||
``` | ||
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## Code Execution | ||
TO DO |
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