no code implementations • 4 Jul 2023 • Eunju Yang, Gyusang Cho, Chan-Hyun Youn
For a more practical scenario of model adaptation from a service provider's point of view, we relax these constraints and present a novel problem scenario of Three-Free Domain Adaptation, namely TFDA, where 1) target labels, 2) source dataset, and mostly 3) source domain information (domain labels + the number of domains) are unavailable.
no code implementations • 29 Sep 2021 • Minsu Jeon, Kyungno Joo, Changha Lee, Taewoo Kim, SeongHwan Kim, Chan-Hyun Youn
In a restricted computing environment like satellite on-board systems, running DL models has limitation on high-speed processing due to the problems such as restriction of available power to consume compared to the relatively high computational complexity.
no code implementations • 29 Sep 2021 • Chaoning Zhang, Gyusang Cho, Philipp Benz, Kang Zhang, Chenshuang Zhang, Chan-Hyun Youn, In So Kweon
The transferability of adversarial examples (AE); known as adversarial transferability, has attracted significant attention because it can be exploited for TransferableBlack-box Attacks (TBA).
no code implementations • 25 Sep 2019 • Heejae Kim, Taewoo Kim, Chan-Hyun Youn
Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e. g., mobile-cloud ecosystems.