多卡分布式框架，让图像删忆实验更快更可复现。
SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation
- 用多GPU并行处理训练-删忆-评估全流程
- 十次随机种子测试，支持全类别与抽样删忆
- 开源框架，适配多种模型与评价指标
机器删忆可在不从头训练的前提下消除特定训练数据对模型的影响。现有图像分类删忆框架仅支持单卡运行，导致多随机种子重复实验耗时过长。我们提出SUPREME，一个开源的多GPU分布式框架，将训练、删忆和评估阶段分布到多个GPU上。该框架具备三大贡献：基于注册表的设计，支持灵活添加新方法、指标、模型与场景；多GPU架构支持多种加速器与精度模式；在Pins Face Recognition数据集上，使用ResNet18和ViT模型，在十次随机种子下完成了全类别与随机样本删忆的验证。代码已公开于https://github.com/pedroandreou/supreme-unlearning。
原文摘要 · Abstract (English)
Machine unlearning removes the influence of specific training data from a trained model without retraining it from scratch. Evaluating an unlearning method requires repeating training, unlearning, and evaluation across multiple seeds, which is computationally expensive. To our knowledge, existing image classification unlearning frameworks run on a single GPU, which limits how many seeds can be evaluated in reasonable time. We introduce SUPREME, an open-source framework that distributes these stages across multiple GPUs. SUPREME makes three contributions: a registry-based design for adding new methods, metrics, models, and scenarios; a multi-GPU architecture supporting multiple accelerators and precision modes; and a demonstration on Pins Face Recognition using ResNet18 and ViT under full-class and random-sample unlearning across ten seeds. The framework is available at https://github.com/pedroandreou/supreme-unlearning.
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