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Achievement: Arctic Code Vault ContributorAchievement: Pull SharkAchievement: Starstruck

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YeongHyeon/README.md

  stat

[CV][Google Scholar]

Papers

SCIE

  • [2024] YeongHyeon Park, Sungho Kang, Myung Jin Kim, Yeonho Lee, Hyeong Seok Kim, and Juneho Yi. "Visual Defect Obfuscation Based Self-Supervised Anomaly Detection." Scientific Reports [paper][poster]
  • [2023] YeongHyeon Park, Myung Jin Kim, Uju Gim, and Juneho Yi. "Boost-up Efficiency of Defective Solar Panel Detection with Pre-trained Attention Recycling." IEEE T-IA [paper][slide]
  • [2022] YeongHyeon Park and JongHee Jung. "Efficient Non-Compression Auto-Encoder for Driving Noise-Based Road Surface Anomaly Detection." IEEJ T-EEE [paper]
  • [2020] YeongHyeon Park, Won Seok Park, and Yeong Beom Kim. "Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network." ETRIJ [paper]
  • [2020] YeongHyeon Park, Il Dong Yun, and Si-Hyuck Kang. "The CNN-based Coronary Occlusion Site Localization with Effective Preprocessing Method." IEEJ T-EEE [paper]
  • [2019] YeongHyeon Park, Il Dong Yun, and Si-Hyuck Kang. "Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG." IEEE Access [paper]
  • [2019] YeongHyeon Park and Il Dong Yun. "Arrhythmia detection in electrocardiogram based on recurrent neural network encoder–decoder with Lyapunov exponent." IEEJ T-EEE [paper]
  • [2018] YeongHyeon Park and Il Dong Yun. "Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine." Sensors [paper]

International Conference

  • [2024] YeongHyeon Park, Sungho Kang, Myung Jin Kim, Yeonho Lee, and Juneho Yi. "Exploiting Connection-Switching U-Net for Enhancing Surface Anomaly Detection." IEEE ICECIE [paper][slide]
  • [2024] YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeonho Jeong, Hyunkyu Park, Hyeong Seok Kim, and Juneho Yi. "Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification." IEEE ICASSP [paper][poster]
  • [2024] Hanbyul Lee*, YeongHyeon Park*, and Juneho Yi. "Enhancing Defective Solar Panel Detection with Attention-guided Statistical Features using Pre-trained Neural Networks." IEEE BigComp [paper] (* Equal contribution)
  • [2023] YeongHyeon Park, Uju Gim, and Myung Jin Kim. "Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection." IEEE ICTC [paper][slide]
  • [2023] Sungho Kang, Hyunkyu Park, YeongHyeon Park, Yeonho Lee, Hanbyul Lee, Seho Bae, and Juneho Yi. "Exploiting Monocular Depth Estimation for Style Harmonization in Landscape Painting." IEEE ICKII [paper]
  • [2023] Hyunkyu Park, Sungho Kang, YeongHyeon Park, Yeonho Lee, Hanbyul Lee, Seho Bae, and Juneho Yi. "Unsupervised Image-to-Image Translation Based on Bidirectional Style Transfer." IEEE ICKII [paper]
  • [2023] YeongHyeon Park, Myung Jin Kim, Won Seok Park, and Juneho Yi. "Recycling for Recycling: RoI Cropping by Recycling a Pre-trained Attention Mechanism for Accurate Classification of Recyclables." IEEE SIST [paper][slide]
  • [2023] YeongHyeon Park, Myung Jin Kim, and Won Seok Park. "Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance." IEEE BigComp [paper][slide]
  • [2022] YeongHyeon Park, Myung Jin Kim, and Uju Gim. "Attention! Is Recycling Artificial Neural Network Effective for Maintaining Renewable Energy Efficiency?" IEEE TPEC [paper][slide]
  • [2021] YeongHyeon Park and JongHee Jung. "Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise." IEEE ICACEH [paper]
  • [2021] YeongHyeon Park and Myung Jin Kim. "Design of Cost-Effective Auto-Encoder for Electric Motor Anomaly Detection in Resource Constrained Edge Device." IEEE ECICE [paper]

Domestic Conference

  • [2024] 박영현, 강성호, 김명진, 이연호, 이준호. "Connection-Switching U-Net을 활용하는 표면이상탐지 성능 향상" 한국방송미디어공학회 2024년 하계학술대회 (2024 The Korean Institute of Broadcast and Media Engineers Summer Conference) [paper]
  • [2023] 김재선, 박춘우, 박원석, 박영현, 조창현, 김동주. "공정 매개변수 및 열화상 이미지를 기반으로 한 다공성 결함 감지를 위한 고압 다이캐스팅 결함 예측 딥러닝 알고리즘에 관한 연구" [paper]
  • [2023] 박영현, 김명진, 박원석, 이준호. "재활용품 분류 자동화 효율증대를 위한 어텐션 메커니즘 기반 객체분할 방법"
  • [2023] 강성호, 박현규, 정현호, 박영현, 배세호, 이준호. "단안 영상 깊이 추정을 활용하는 객체 변환 합성"
  • [2023] 박현규, 배세호, 박영현, 강성호, 이준호. "양방향 스타일 변환 네트워크를 사용하는 비지도 학습 기반의 도메인 간 영상 변환"
  • [2023] 김명진, 박영현, 윤일동. "적대적 학습에서 긍정 샘플의 선정에 대한 기법"
  • [2022] 김우주, 박영현. "이상 탐지를 위한 오토인코더 기반 잠재 벡터 확장" [arXiv]
  • [2022] 박영현, 이준성, 김명진, 박원석. "주행 소음 기반 도로 이상탐지 성능 향상을 위한 주행 이벤트 추출 및 노이즈 감쇄 방법" [arXiv]
  • [2022] 김명진, 박영현. "Attention 기반의 이상 부위 자동 labeling 기법"
  • [2021] 박영현, 이준성, 박원석. "신뢰도 기반 개별 모델 영향력을 조정하는 자체 가중치 앙상블 방법" [arXiv]

Preprints

  • [2024] YeongHyeon Park, Myung Jin Kim, Hyeong Seok Kim. "Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection" [arXiv]
  • [2024] YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeong Seok Kim, and Juneho Yi. "Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection" [arXiv]
  • [2024] Dongeon Kim, YeongHyeon Park. "Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods" [arXiv]
  • [2022] YeongHyeon Park. "Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model" [arXiv]
  • [2018] YeongHyeon Park and Il Dong Yun. "Comparison of RNN Encoder-Decoder Models for Anomaly Detection" [arXiv]
Repositories 
Repositories  
│
├── TensorFlow 
│    ├── Publications (Sorted by year in ascending order)
│    │    ├── Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG
│    │    │    ├── IEEE Access (2019): https://ieeexplore.ieee.org/abstract/document/8771175
│    │    │    └── Source: https://github.com/YeongHyeon/Preprocessing-Method-for-STEMI-Detection
│    │    ├── Arrhythmia detection in electrocardiogram based on recurrent neural network encoder–decoder with Lyapunov exponent
│    │    │    ├── IEEJ (2018): https://onlinelibrary.wiley.com/doi/abs/10.1002/tee.22927
│    │    │    └── Source: https://github.com/YeongHyeon/Arrhythmia_Detection_RNN_and_Lyapunov
│    │    └── Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine
│    │         ├── MDPI (2018): https://www.mdpi.com/1424-8220/18/10/3573
│    │         └── Source: https://github.com/YeongHyeon/FARED_for_Anomaly_Detection
│    │  
│    ├── Discriminative Model
│    │    ├── Series Inception
│    │    │    ├── Inception: https://github.com/YeongHyeon/Inception_Simplified-TF2
│    │    │    └── XCeption: https://github.com/YeongHyeon/XCeption-TF2
│    │    ├── Series Residual
│    │    │    ├── ResNet: https://github.com/YeongHyeon/ResNet-TF2
│    │    │    ├── ResNeXt: https://github.com/YeongHyeon/ResNeXt-TF2
│    │    │    ├── WRN: https://github.com/YeongHyeon/WideResNet_WRN-TF2
│    │    │    ├── ResNeSt: https://github.com/YeongHyeon/ResNeSt-TF2
│    │    │    └── ReXNet: https://github.com/YeongHyeon/ReXNet-TF2
│    │    ├── Series Bayesian / Gaussian
│    │    │    └── SWA-Gaussian: https://github.com/YeongHyeon/SWA-Gaussian-TF2
│    │    ├── Series Graph
│    │    │    └── PIPGCN: https://github.com/YeongHyeon/PIPGCN-TF2
│    │    └── Ohters
│    │         ├── SE-Net: https://github.com/YeongHyeon/SENet-Simple
│    │         ├── SK-Net: https://github.com/YeongHyeon/SKNet-TF2
│    │         ├── GhostNet: https://github.com/YeongHyeon/GhostNet
│    │         ├── Network-in-Network: https://github.com/YeongHyeon/Network-in-Network-TF2
│    │         ├── Shake-Shake Regularization: https://github.com/YeongHyeon/Shake-Shake
│    │         ├── MNIST Attention Map: https://github.com/YeongHyeon/MNIST_AttentionMap
│    │         └── MLP-Mixer: https://github.com/YeongHyeon/MLP-Mixer-TF2
│    │    
│    ├── Generative Model
│    │    ├── Generals
│    │    │    ├── GAN: https://github.com/YeongHyeon/GAN-TF
│    │    │    ├── WGAN: https://github.com/YeongHyeon/WGAN-TF
│    │    │    ├── CGAN: https://github.com/YeongHyeon/CGAN-TF
│    │    │    ├── Normalizing Flow: https://github.com/YeongHyeon/Normalizing-Flow-TF2
│    │    │    └── Transformer: https://github.com/YeongHyeon/Transformer-TF2
│    │    ├── Anomaly Detection
│    │    │    ├── CVAE (Convolution & Variational): https://github.com/YeongHyeon/CVAE-AnomalyDetection
│    │    │    ├── GANomaly: https://github.com/YeongHyeon/GANomaly-TF
│    │    │    ├── Skip-GANomaly: https://github.com/YeongHyeon/Skip-GANomaly
│    │    │    ├── ConAD: https://github.com/YeongHyeon/ConAD
│    │    │    ├── MemAE: https://github.com/YeongHyeon/MemAE
│    │    │    ├── f-AnoGAN: https://github.com/YeongHyeon/f-AnoGAN-TF
│    │    │    ├── DGM: https://github.com/YeongHyeon/DGM-TF
│    │    │    └── ADAE: https://github.com/YeongHyeon/ADAE-TF
│    │    └── Special Purpose
│    │         ├── SRCNN: https://github.com/YeongHyeon/Super-Resolution_CNN
│    │         ├── Context-Encoder: https://github.com/YeongHyeon/Context-Encoder
│    │         └── Sequence-Autoencoder: https://github.com/YeongHyeon/Sequence-Autoencoder
│    │    
│    └── Additional Methods
│         ├── SGDR: https://github.com/YeongHyeon/ResNet-with-SGDR-TF2
│         ├── Learning rate WarmUp: https://github.com/YeongHyeon/ResNet-with-LRWarmUp-TF2
│         └── ArcFace: https://github.com/YeongHyeon/ArcFace-TF2
│
└── PyTorch
     ├── Discriminative Model
     │    └── Ohters
     │         ├── MLP-Mixer: https://github.com/YeongHyeon/MLP-Mixer-PyTorch
     │         ├── GhostNet: https://github.com/YeongHyeon/GhostNet-PyTorch
     │         └── DINO: https://github.com/YeongHyeon/DINO_MNIST-PyTorch
     └── Generative Model
          ├── Anomaly Detection
          │    ├── CVAE (Convolution & Variational): https://github.com/YeongHyeon/CVAE-AnomalyDetection-PyTorch
          │    ├── GANomaly: https://github.com/YeongHyeon/GANomaly-PyTorch
          │    ├── ConAD: https://github.com/YeongHyeon/ConAD-PyTorch
          │    └── RIAD: https://github.com/YeongHyeon/RIAD-PyTorch
          └── Special Purpose
               └── SRCNN: https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch
Kaggle

Notebooks Expert 🎓

Competition

  • 🥉 RSNA 2023 Abdominal Trauma Detection

Datasets

Pinned Loading

  1. R-CNN_LIGHT R-CNN_LIGHT Public

    Regional-Convolution Neural Network for blink detection based on contouring.

    Python 66 22

  2. ResNeSt-TF2 ResNeSt-TF2 Public

    TensorFlow implementation of "ResNeSt: Split-Attention Networks"

    Python 67 17

  3. Super-Resolution_CNN Super-Resolution_CNN Public

    Implementation of "Image Super-Resolution using Deep Convolutional Network"

    Python 44 13

  4. FARED_for_Anomaly_Detection FARED_for_Anomaly_Detection Public

    Official source code of "Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly Machine"

    Python 17 4

  5. MemAE-TF2 MemAE-TF2 Public

    TensorFlow implementation of "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection"

    Python 25 6

  6. SWA-Gaussian-TF2 SWA-Gaussian-TF2 Public

    TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

    Python 8