117 citations · 443 across the 36 of their papers we have counts for
5 papers · 1 filter
Uncertainty-based Continual Learning with Adaptive Regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee +1
We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learni…
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
Sungmin Cha, Taeeon Park, Byeongjoon Kim +2
We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, ex…
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling
Sunghwan Joo, Sungmin Cha, Taesup Moon
We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural a…
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
Hongjoon Ahn, Taesup Moon
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our a…
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo, Sunghwan Joo, Taesup Moon
We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter…