Machine Unlearning (MU) aims to remove target training data from a trained model so that the removed data no longer influences the model's behavior, fulfilling "right to be forgotten" obligations under data privacy laws. Yet, we observe that researchers in this rapidly emerging field face challenges in analyzing and understanding the behavior of different MU methods, especially in terms of three fundamental principles in MU: accuracy, efficiency, and privacy. Consequently, they often rely on aggregate metrics and ad-hoc evaluations, making it difficult to accurately assess the trade-offs between methods. To fill this gap, we introduce a visual analytics system, Unlearning Comparator, designed to facilitate the systematic evaluation of MU methods. Our system supports two important tasks in the evaluation process: model comparison and attack simulation. First, it allows the user to compare the behaviors of two models, such as a model generated by a certain method and a retrained baseline, at class-, instance-, and layer-levels to better understand the changes made after unlearning. Second, our system simulates membership inference attacks (MIAs) to evaluate the privacy of a method, where an attacker attempts to determine whether specific data samples were part of the original training set. We evaluate our system through a case study visually analyzing prominent MU methods and demonstrate that it helps the user not only understand model behaviors but also gain insights that can inform the improvement of MU methods.

Diffusion models, which are state-of-the-art generative models, have been widely applied to image restoration tasks. However, most image restoration methods based on diffusion models require a large amount of computational memory, making it difficult to use them with high-resolution images. Although patch-based diffusion models have emerged to address this problem, these models are limited in effectively mitigating boundary artifacts and producing results close to the ground truth. In this paper, we propose Patch-based Diffusion using Constrained Refinement (PD-CR) that refines the noise estimated by patch-based diffusion models to produce a restored image while keeping the luminance of the input degraded image. Leveraging patch-based diffusion models, the proposed method can handle a high-resolution image as input with minimal memory requirements. Our experiments on various image restoration tasks, such as image denoising and raindrop removal, demonstrate that the proposed method is better than or on par with the state-of-the-art methods.

Due to the significant power consumption of emissive display panels and the limited battery capacity of electronic devices, it is necessary to implement power-constrained display operations for video content. One of the effective power-constrained display techniques is power-constrained contrast enhancement (PCCE), which aims to reduce the power demands of the display while maintaining the quality of the content. However, PCCE has been mainly studied in the image domain. In this letter, we explore how PCCE can be applied to the video domain by revisiting the proportional-integral-derivative (PID) control. The proposed method consists of three key components: 1) The proportional term determines a power-saving ratio for each video frame based on its luminance, resulting in aggressive power-saving in bright video frames; 2) the integral term ensures that the target power-saving ratio is achieved over the entire video; 3) the differential term prevents abrupt temporal fluctuations in the power-saving ratio of consecutive video frames. The experimental results demonstrate that the proposed PID-based PCCE method achieves superior performance on two public video datasets. In particular, the proposed method achieves 5.8% improvements in VMAF compared to the previous method on the TVSum dataset.