14 citations · 20 across the 4 of their papers we have counts for
6 papers
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks
Jaehee Jang, Heonseok Ha, Dahuin Jung +1
Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner…
iCaps: An Interpretable Classifier via Disentangled Capsule Networks
Dahuin Jung, Jonghyun Lee, Jihun Yi +1
We propose an interpretable Capsule Network, iCaps, for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a cl…
PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation
Dahuin Jung, Ho Bae, Hyun-Soo Choi +1
Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego ima…
HexaGAN: Generative Adversarial Nets for Real World Classification
Uiwon Hwang, Dahuin Jung, Sungroh Yoon
Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance…
AnomiGAN: Generative adversarial networks for anonymizing private medical data
Ho Bae, Dahuin Jung, Sungroh Yoon
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence…
Security and Privacy Issues in Deep Learning
Ho Bae, Jaehee Jang, Dahuin Jung +4
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended w…