4 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective
HyeongGwon Hong, Yooshin Cho, Hanbyel Cho +2
Gradient inversion attacks can leak data privacy when clients share weight updates with the server in federated learning (FL). Existing studies mainly use L2 or cosine distance as…
Disposable Transfer Learning for Selective Source Task Unlearning
Seunghee Koh, Hyounguk Shon, Janghyeon Lee +2
Transfer learning is widely used for training deep neural networks (DNN) for building a powerful representation. Even after the pre-trained model is adapted for the target task, th…
Data Poisoning Attack Aiming the Vulnerability of Continual Learning
Gyojin Han, Jaehyun Choi, Hyeong Gwon Hong +1
Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world constraints related to memory and privacy. However, this…
Continual Learning with Extended Kronecker-factored Approximate Curvature
Janghyeon Lee, Hyeong Gwon Hong, Donggyu Joo +1
We propose a quadratic penalty method for continual learning of neural networks that contain batch normalization (BN) layers. The Hessian of a loss function represents the curvatur…
Residual Continual Learning
Janghyeon Lee, Donggyu Joo, Hyeong Gwon Hong +1
We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of mul…