162 citations · 303 across the 20 of their papers we have counts for
7 papers · 1 filter
Joint Negative and Positive Learning for Noisy Labels
Youngdong Kim, Juseung Yun, Hyounguk Shon +1
Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive L…
Extending Contrastive Learning to Unsupervised Coreset Selection
Jeongwoo Ju, Heechul Jung, Yoonju Oh +1
Self-supervised contrastive learning offers a means of learning informative features from a pool of unlabeled data. In this paper, we delve into another useful approach -- providin…
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…
NLNL: Negative Learning for Noisy Labels
Youngdong Kim, Junho Yim, Juseung Yun +1
Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised m…
Less-forgetful Learning for Domain Expansion in Deep Neural Networks
Heechul Jung, Jeongwoo Ju, Minju Jung +1
Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned inf…