3 papers
cs.LG2025
Federated Learning via Meta-Variational Dropout
Insu Jeon, Minui Hong, Junhyeog Yun +1
Federated Learning (FL) aims to train a global inference model from remotely distributed clients, gaining popularity due to its benefit of improving data privacy. However, traditio…
cs.CV2025
IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks
Insu Jeon, Wonkwang Lee, Myeongjang Pyeon +1
We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framewo…
cs.LG2025
Neural Variational Dropout Processes
Insu Jeon, Youngjin Park, Gunhee Kim
Learning to infer the conditional posterior model is a key step for robust meta-learning. This paper presents a new Bayesian meta-learning approach called Neural Variational Dropou…