ScholarWorks@Dongguk University: Lee, Woo jin

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Lee, Woo jin (이우진)

College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)

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Tel 02-2260-3787
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Adversarial attack:4||Adversarial training:2||Imputation:2||Adversarial defense:2||Cycleconsistent:2||Missing data:2||CLIP:1||Class collision:1||Computational efficiency:1||Consistency regularization:1||Contrastive learning:1||Convolutional Neural Networks (CNN):1||Data models:1||Dataset refinement:1||Deep learning:1||Detection:1||Ensemble:1||Fair clustering:1||Fair distribution:1||Fault diagnosis:1||Flood susceptibility:1||Focusing:1||Fourier transform:1||GIS:1||Geospatial Analysis:1||Graph neural network:1||Graph representation learning:1||Group Method of data Handling (GMDH):1||Head:1||Heavilytailed distribution:1||Indoor Radon Level:1||Interviews:1||LSTM:1||Long shortterm Memory (LSTM):1||Longtailed learning:1||Machine learning:1||Machine learning algorithms:1||Multi label classification:1||Network analysis:1||Neural networks:1||Noisy label:1||Optimization:1||Periodic time series:1||Prediction algorithms:1||Predictive models:1||Relabeling:1||Recruitment:1||Robust loss function:1||Rotating machinery:1||SVR:1||Sharpnessaware minimization:1||Sliced Wasserstein Distance:1||Spectrogram:1||Support vector clustering:1||Sustainable cryptocurrency:1||Tail:1||Time series:1||Time series classification:1||Time series forecasting:1||Training:1||Training data:1||Transfer entropy:1||Tuning:1||Visualization:1||Wasserstein distance:1||adversarial:1||asymmetric loss:1||automatic interview assessment:1||balanced asymmetric loss:1||class imbalance:1||contrastive learning:1||fairness:1||imbalanced sampling:1||loss functions:1||multilabel classification:1||multimodal:1||representation learning:1||sensitive attribute:1||unfair assumption:1||Adaptation models:1||visionlanguage models:1||Adversarial robustness:1||Artificial Intelligence (AI):1||Bearing fault diagnosis system:1||Blackbox setting:1||Boosted tree:1||

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