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.

Recently, serious concerns have been raised about the privacy issues related to training datasets in machine learning algorithms when including personal data. Various regulations in different countries, including the GDPR grant individuals to have personal data erased, known as ‘the right to be forgotten’ or ‘the right to erasure’. However, there has been less research on effectively and practically deleting the requested personal data from the training set while not jeopardizing the overall machine learning performance. In this work, we propose a fast and novel machine unlearning paradigm at the layer level called layer attack unlearning, which is highly accurate and fast compared to existing machine unlearning algorithms. We introduce the Partial-PGD algorithm to locate the samples to forget efficiently. In addition, we only use the last layer of the model inspired by the Forward-Forward algorithm for unlearning process. Lastly, we use Knowledge Distillation (KD) to reliably learn the decision boundaries from the teacher using soft label information to improve accuracy performance. We conducted extensive experiments with SOTA machine unlearning models and demonstrated the effectiveness of our approach for accuracy and end-to-end unlearning performance.

Machine learning has evolved through extensive data usage, including personal and private information. Regulations like GDPR highlight the "Right to be forgotten" for user and data privacy. Research in machine unlearning aims to remove specific data from pre-trained models. We introduce a novel two-step unlearning method, UNDO. First, we selectively disrupt the decision boundary of forgetting data at the coarse-grained level. However, this can also inadvertently affect the decision boundary of other remaining data, lowering the overall performance of classification task. Hence, we subsequently repair and refining the decision boundary for each class at the fine-grained level by introducing a loss for maintain the overall performance, while completely removing the class. Our approach is validated through experiments on two datasets, outperforming other methods in effectiveness and efficiency.

Abstract In this paper, we propose multi‐layer structural wound synthesis on a 3D face. The fundamental knowledge of the facial skin is derived from the structure of tissue, being composed of epidermis, dermis and subcutis. The approach first defines the facial tissue depth map to measure details at various locations on the face. Each layer of skin in a wound image has been determined by hue‐based segmentation. In addition, we have employed disparity parameters to realise 3D depth in order to make a wound model volumetric. Finally, we validate our methods using 3D wound simulation experiments. Copyright © 2011 John Wiley & Sons, Ltd.

This paper introduces a humanoid robot system for evaluating human-robot interaction. A humanoid robot. Kibo. is designed to walk and dance with 22 joints and specially has a robot face to express its emotion by moving lips and eyebrows, etc. To evaluate emotional states of a human operator, face and gesture expressions are recognized by external stereo vision system and speech commands are recognized with a wireless microphone. On the view point of software development. Intelligent Robot Software Architecture developed by Center for Intelligent Robotics is adopted. Since the software architecture is designed for reusability and extensibility, rapid S/W development was achieved. As primary experiments. Kibo imitates human operator's facial and gesture expression.