22 citations · 44 across the 4 of their papers we have counts for
4 papers
Confidence Score for Source-Free Unsupervised Domain Adaptation
Jonghyun Lee, Dahuin Jung, Junho Yim +1
Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing…
Hyperparameter Optimization with Neural Network Pruning
Kangil Lee, Junho Yim
Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it take…
Highway Driving Dataset for Semantic Video Segmentation
Byungju Kim, Junho Yim, Junmo Kim
Scene understanding is an essential technique in semantic segmentation. Although there exist several datasets that can be used for semantic segmentation, they are mainly focused on…
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…