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pro vyhledávání: '"CHAN, CHEE SENG"'
This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), an area that has received limited attention compared to horizontal federated learning. We introduce the first approach specifically designed to tackle lab
Externí odkaz:
http://arxiv.org/abs/2410.10922
The advent of Federated Learning (FL) highlights the practical necessity for the 'right to be forgotten' for all clients, allowing them to request data deletion from the machine learning model's service provider. This necessity has spurred a growing
Externí odkaz:
http://arxiv.org/abs/2405.17462
Autor:
Lin, Che-Tsung, Ng, Chun Chet, Tan, Zhi Qin, Nah, Wan Jun, Wang, Xinyu, Kew, Jie Long, Hsu, Pohao, Lai, Shang Hong, Chan, Chee Seng, Zach, Christopher
Extremely low-light text images are common in natural scenes, making scene text detection and recognition challenging. One solution is to enhance these images using low-light image enhancement methods before text extraction. However, previous methods
Externí odkaz:
http://arxiv.org/abs/2404.14135
Autor:
Sii, Jia Wei, Chan, Chee Seng
Contemporary makeup transfer methods primarily focus on replicating makeup from one face to another, considerably limiting their use in creating diverse and creative character makeup essential for visual storytelling. Such methods typically fail to a
Externí odkaz:
http://arxiv.org/abs/2404.13944
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressive demonstrations. Since then, technopreneurs have sought to leverage
Externí odkaz:
http://arxiv.org/abs/2401.09495
Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions, enabling vast applications in content generation. While recent advancements have introduced control o
Externí odkaz:
http://arxiv.org/abs/2312.05849
Autor:
Yang, Sze Jue, La, Chinh D., Nguyen, Quang H., Wong, Kok-Seng, Tran, Anh Tuan, Chan, Chee Seng, Doan, Khoa D.
Backdoor attacks, representing an emerging threat to the integrity of deep neural networks, have garnered significant attention due to their ability to compromise deep learning systems clandestinely. While numerous backdoor attacks occur within the d
Externí odkaz:
http://arxiv.org/abs/2312.03419
The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications. Conventional wisdom suggests that not everyone can be an attacker since the process of designing the trigger ge
Externí odkaz:
http://arxiv.org/abs/2308.16684
Autor:
Ng, Kam Woh, Zhu, Xiatian, Hoe, Jiun Tian, Chan, Chee Seng, Zhang, Tianyu, Song, Yi-Zhe, Xiang, Tao
Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often overlook the fact that the similarity between data points in the continuou
Externí odkaz:
http://arxiv.org/abs/2302.07669