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
└── 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
└── Special Purpose
└── SRCNN: https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch