Search Results for author: Chan-Hyun Youn

Found 4 papers, 0 papers with code

FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalization

no code implementations4 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.

Domain Adaptation Philosophy

Ambiguity Adaptive Inference and Single-shot based Channel Pruning for Satellite Processing Environments

no code implementations29 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.

Early Stop And Adversarial Training Yield Better surrogate Model: Very Non-Robust Features Harm Adversarial Transferability

no code implementations29 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).


On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods

no code implementations25 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.

Federated Learning Hyperparameter Optimization

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