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SUPREME addresses a critical bottleneck in machine unlearning research by enabling efficient evaluation across multiple GPU seeds, which is essential for rigorous statistical validation of privacy-preserving methods. This framework advances the reproducibility and scalability of image classification models undergoing specific data removal without full retraining. It represents significant progress in making high-fidelity unlearning benchmarks accessible to the broader AI community.
SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation
arXiv:2606.00380v2 Announce Type: replace-cross Abstract: 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.
Abstract: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 this https URL.
Accepted at WIPE-OUT 2026, the 2nd Workshop on Machine Unlearning and Privacy Preservation, co-located with ECML-PKDD 2026, Naples, Italy. Camera-ready version. 16 pages. Code available at this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2606.00380 [cs.CV]
(or arXiv:2606.00380v2 [cs.CV] for this version)
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