Search Results for author: Junho Yim

Found 8 papers, 3 papers with code

Confidence Score for Source-Free Unsupervised Domain Adaptation

1 code implementation14 Jun 2022 Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon

Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge.

Unsupervised Domain Adaptation

Hyperparameter Optimization with Neural Network Pruning

no code implementations18 May 2022 Kangil Lee, Junho Yim

However, the huge time consumption of hyperparameter optimization due to the high computational cost of the deep learning model itself has not been dealt with in-depth.

Hyperparameter Optimization Network Pruning

Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation

no code implementations29 Sep 2021 Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon

Unsupervised domain adaptation (UDA) aims to achieve high performance within the unlabeled target domain by leveraging the labeled source domain.

Pseudo Label Unsupervised Domain Adaptation

Highway Driving Dataset for Semantic Video Segmentation

no code implementations2 Nov 2020 Byungju Kim, Junho Yim, Junmo Kim

Together with our attempt to analyze the temporal correlation, we expect the Highway Driving dataset to encourage research on semantic video segmentation.

Autonomous Driving Image Segmentation +4

NLNL: Negative Learning for Noisy Labels

1 code implementation ICCV 2019 Youngdong Kim, Junho Yim, Juseung Yun, Junmo Kim

The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned correctly to all images.

General Classification Image Classification

A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning

1 code implementation CVPR 2017 Junho Yim, Donggyu Joo, Jihoon Bae, Junmo Kim

We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN.

Knowledge Distillation Transfer Learning

Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition

no code implementations ICCV 2015 Heechul Jung, Sihaeng Lee, Junho Yim, Sunjeong Park, Junmo Kim

Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.

Facial Expression Recognition (FER)

Rotating Your Face Using Multi-Task Deep Neural Network

no code implementations CVPR 2015 Junho Yim, Heechul Jung, ByungIn Yoo, Changkyu Choi, Dusik Park, Junmo Kim

This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity.

Face Recognition

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