Archive_discrete_knowledge

Basic knowledge in neural networks

1. Graph Neural Network
2. Graph Convolutional Network
3. Transform learning: mete learning, few shot learning, zero-shot learning
4. RNN, GRU
5. Siamese Network
6. Reinforcement learning
7. NAS
8. Optimization: Adam, SGD
9. Normalization
10. AlexNet, VGG, Inception, ResNet (ResNet-50,ResNet-101, ResNeXt-50/101), Xecption.

SOTA research areas

1. Object recognition/detection: YOLO, LOGAN, Anchor, Anchor free, two-stage,one-stage
2. Object tracking
3. face recognition/detection
4. NLP: BERT, seq2seq, bag of words
5. Image segmentation/instance segmentation: deeplab
6. ShuffleNet, MobileNet
7. 2D Image —> 3D model
8. Human pose estimation: 2D skeleton, 3D skeleton, 3D mesh
9. Optical flow
10. Attention
11. FPN, Mask-rcnn,faster-rcnn, RPN, RetinaNet, ROI pooling,

Machine Learning

1. KNN,SVM, GBDT, XGBOOST