4 citations · 7 across the 9 of their papers we have counts for
9 papers
Controllable Feature Whitening for Hyperparameter-Free Bias Mitigation
Yooshin Cho, Hanbyel Cho, Janghyeon Lee +3
As the use of artificial intelligence rapidly increases, the development of trustworthy artificial intelligence has become important. However, recent studies have shown that deep n…
Self-supervised Transformation Learning for Equivariant Representations
Jaemyung Yu, Jaehyun Choi, Dong-Jae Lee +2
Unsupervised representation learning has significantly advanced various machine learning tasks. In the computer vision domain, state-of-the-art approaches utilize transformations l…
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…
Localization using Multi-Focal Spatial Attention for Masked Face Recognition
Yooshin Cho, Hanbyel Cho, Hyeong Gwon Hong +4
Since the beginning of world-wide COVID-19 pandemic, facial masks have been recommended to limit the spread of the disease. However, these masks hide certain facial attributes. Hen…
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…
EDAS: Efficient and Differentiable Architecture Search
Hyeong Gwon Hong, Pyunghwan Ahn, Junmo Kim
Transferrable neural architecture search can be viewed as a binary optimization problem where a single optimal path should be selected among candidate paths in each edge within the…