Internet Service Domain
The internet service domain encompasses a broad range of research focused on improving the efficiency and effectiveness of online services. Current research emphasizes adapting models to specific domains, often using techniques like domain adaptation, knowledge distillation, and domain decomposition, frequently incorporating architectures such as U-Nets, transformers, and BERT variants. These advancements aim to enhance performance in tasks like question answering, image segmentation, and speech recognition, while also addressing challenges such as data imbalance, domain shifts, and adversarial attacks. Ultimately, this research strives to create more robust, efficient, and reliable online services across diverse applications.
Papers
Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning
Shicheng Zhou, Jingju Liu, Yuliang Lu, Jiahai Yang, Yue Zhang, Jie Chen
SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation Learning
Seokju Yun, Seunghye Chae, Dongheon Lee, Youngmin Ro