Search Results for author: Juyoung Lee

Found 4 papers, 1 papers with code

Improving Evaluation of Debiasing in Image Classification

no code implementations8 Jun 2022 Jungsoo Lee, Juyoung Lee, Sanghun Jung, Jaegul Choo

Based on such issues, this paper 1) proposes an evaluation metric `Align-Conflict (AC) score' for the tuning criterion, 2) includes experimental settings with low bias severity and shows that they are yet to be explored, and 3) unifies the standardized experimental settings to promote fair comparisons between debiasing methods.

Classification Image Classification

Revisiting the Importance of Amplifying Bias for Debiasing

no code implementations29 May 2022 Jungsoo Lee, Jeonghoon Park, Daeyoung Kim, Juyoung Lee, Edward Choi, Jaegul Choo

$f_B$ is trained to focus on bias-aligned samples (i. e., overfitted to the bias) while $f_D$ is mainly trained with bias-conflicting samples by concentrating on samples which $f_B$ fails to learn, leading $f_D$ to be less susceptible to the dataset bias.

Attribute Image Classification

Learning Debiased Representation via Disentangled Feature Augmentation

1 code implementation NeurIPS 2021 Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, Jaegul Choo

To this end, our method learns the disentangled representation of (1) the intrinsic attributes (i. e., those inherently defining a certain class) and (2) bias attributes (i. e., peripheral attributes causing the bias), from a large number of bias-aligned samples, the bias attributes of which have strong correlation with the target variable.

Data Augmentation Image Classification

k-Space Deep Learning for Reference-free EPI Ghost Correction

no code implementations1 Jun 2018 Juyoung Lee, Yoseob Han, Jae-Kyun Ryu, Jang-Yeon Park, Jong Chul Ye

Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster. The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.

Matrix Completion

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