5 citations · 13 across the 8 of their papers we have counts for
6 papers · 1 filter
FedHide: Federated Learning by Hiding in the Neighbors
Hyunsin Park, Sungrack Yun
We propose a prototype-based federated learning method designed for embedding networks in classification or verification tasks. Our focus is on scenarios where each client has data…
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park +3
Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their…
Federated Learning of User Verification Models Without Sharing Embeddings
Hossein Hosseini, Hyunsin Park, Sungrack Yun +3
We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be share…
Federated Learning of User Authentication Models
Hossein Hosseini, Sungrack Yun, Hyunsin Park +3
Machine learning-based User Authentication (UA) models have been widely deployed in smart devices. UA models are trained to map input data of different users to highly separable em…
Meta-Learning via Feature-Label Memory Network
Dawit Mureja, Hyunsin Park, Chang D. Yoo
Deep learning typically requires training a very capable architecture using large datasets. However, many important learning problems demand an ability to draw valid inferences fro…
Early Improving Recurrent Elastic Highway Network
Hyunsin Park, Chang D. Yoo
To model time-varying nonlinear temporal dynamics in sequential data, a recurrent network capable of varying and adjusting the recurrence depth between input intervals is examined.…