Ren Wang

Yingdan Shi, Sijia Liu, Ren Wang

Machine unlearning seeks to remove the influence of specified data from a trained model. While metrics such as unlearning accuracy (UA) and membership inference attack (MIA) provide baselines for assessing unlearning performance, they fall short of evaluating the forgetting reliability. In this paper, we find that the data misclassified across UA and MIA still have their ground truth labels included in the prediction set from the uncertainty quantification perspective, which raises a fake unlearning issue. To address this issue, we propose two novel metrics inspired by conformal prediction that more reliably evaluate forgetting quality. Building on these insights, we further propose a conformal prediction-based unlearning framework that integrates conformal prediction into Carlini & Wagner adversarial attack loss, which can significantly push the ground truth label out of the conformal prediction set. Through extensive experiments on image classification task, we demonstrate both the effectiveness of our proposed metrics and the superiority of our unlearning framework, which improves the UA of existing unlearning methods by an average of 6.6% through the incorporation of a tailored loss term alone.

Shao-Hao Lu, Ren Wang, Ching-Chun Huang et al.

Recently, diffusion-based blind super-resolution (SR) methods have shown great ability to generate high-resolution images with abundant high-frequency detail, but the detail is often achieved at the expense of fidelity. Meanwhile, another line of research focusing on rectifying the reverse process of diffusion models (i.e., diffusion guidance), has demonstrated the power to generate high-fidelity results for non-blind SR. However, these methods rely on known degradation kernels, making them difficult to apply to blind SR. To address these issues, we present DADiff in this paper. DADiff incorporates degradation-aware models into the diffusion guidance framework, eliminating the need to know degradation kernels. Additionally, we propose two novel techniques: input perturbation and guidance scalar, to further improve our performance. Extensive experimental results show that our proposed method has superior performance over state-of-the-art methods on blind SR benchmarks.