Recent Publications
- Visual Defect Obfuscation Based Self-Supervised Anomaly Detection
YeongHyeon Park, Sungho Kang, Myung Jin Kim, Yeonho Lee, Hyeong Seok Kim, Juneho Yi
Scientific Reports (Q1), 2024
[paper] [poster] - Boost-up Efficiency of Defective Solar Panel Detection with Pre-trained Attention Recycling
YeongHyeon Park, Myung Jin Kim, Uju Gim, Juneho Yi
IEEE Transactions on Industry Applications (Q1), 2023
[paper] [slide]
- Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeong Seok Kim, Juneho Yi
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2025 VAND3.0 workshop)
[paper] [poster] - Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection
YeongHyeon Park, Myung Jin Kim, Hyeong Seok Kim
IEEE International Symposium on Biomedical Imaging (ISBI 2025)
[paper] [poster] - Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification
YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeonho Jeong, Hyunkyu Park, Hyeong Seok Kim, Juneho Yi
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)
[paper] [poster]
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 🎓
- 🥉 RSNA23 EASY DICOM Confirmation & Volume Generation @ RSNA 2023 Abdominal Trauma Detection
- 🥉 Riiid! step by step guide for Beginner/EDA/PyTorch @ Riiid Answer Correctness Prediction
- 🥉 Easy to run, Keras Full Package! (including EDA) @ [T-Academy X KaKr] 성인 인구조사 소득 예측 대회
- 🥉 Shopee, Easy to Run! @ Shopee - Price Match Guarantee
- 🥉 SETI, step by step guide for Beginner/EDA/TF @ SETI Breakthrough Listen - E.T. Signal Search
- 🥉 Convert DICOM to Numpy Array (Super Simple) @ RSNA-MICCAI Brain Tumor Radiogenomic Classification
- 🥉 Baseline UAD (w/ Fashion MNIST dataset)
- 😆 Step-by-Step MNIST | Full Package, EDA, TensorFlow @ Digit Recognizer
- 😆 Step-by-Step, Herbarium 2021! @ Herbarium 2021 - Half-Earth Challenge - FGVC8
- 🥉 RSNA 2023 Abdominal Trauma Detection
- 😆 RSNA-MICCAI BTRC2021 @ RSNA-MICCAI Brain Tumor Radiogenomic Classification