Machine unlearning has emerged as a significant research topic in response to the increasing demands for data privacy and compliance with privacy regulations. The main challenge is to eliminate the influence of a specific subset of training data from a pretrained model while preserving the model's performance on the retain set without retraining the model from scratch. In this paper, we propose a novel efficient unlearning framework based on Optimal Transport, which can effectively work on both class-wise and instance-wise unlearning tasks. By analyzing and comparing the feature spaces of the original and retrained models, we formulate the unlearning problem as a distribution alignment task between the forget set and the retain set. We guide the feature distribution of the forget set, which initially forms distinct and structured patterns, to align with that of the retain set. Extensive experiments on three public benchmark datasets demonstrate its superior effectiveness compared to previous state-of-the-art methods.

With increasing regulations on private data usage in AI systems, machine unlearning has emerged as a critical solution for selectively removing sensitive information from trained models while preserving their overall utility. While many existing unlearning methods rely on the *retain data* to mitigate the performance decline caused by forgetting, such data may not always be available (*retain-free*) in real-world scenarios. To address this challenge posed by retain-free unlearning, we introduce **RUAGO**, utilizing adversarial soft labels to mitigate over-unlearning and a generative model pretrained on out-of-distribution (OOD) data to effectively distill the original model’s knowledge. We introduce a progressive sampling strategy to incrementally increase synthetic data complexity, coupled with an inversion-based alignment step that ensures the synthetic data closely matches the original training distribution. Our extensive experiments on multiple benchmark datasets and architectures demonstrate that our approach consistently outperforms existing retain-free methods and achieves comparable or superior performance relative to retain-based approaches, demonstrating its effectiveness and practicality in real-world, data-constrained environments.