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Classic Papers
- Deep Residual Learning for Image Recognition :: Pytorch / Tensorflow2
- You Only Look Once: Unified, Real-Time Object Detection
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Attention Is All You Need
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Semi-Supervised Learning
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Generative Models
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Transformers
- Vanilla Transformer
- Vision Transformer (ViT)
- DETR
- DINO
- DeiT
- Swin-Transformer
- MLP-Mixer
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Self-supervised learning
- SimCLR
- BYOL
- MoCo
- SimSiam
- SwaV
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3D vision
- Mesh deformation
- 3D-R2N2
- GAL
- Pixel2Mesh
Techniques for modeling | About |
---|---|
Augmentation libraries | Albumentations and more |
Hyperparameter search | Optuna and more |
AutoML | Pycaret / H2O / lightautoml and more |
Augmentation techniques | Mixup, Cutmix and more |
Ensemble methods | SWA and more |
Class imbalance | Balancedsampler, Oversampling and more |
Loss functions | Arcface and more |
Visualizations | Tensorboard and more |
GPU | GPU |
Pytorch Lightening | Pytorch Lightening |
Mixed/Half precision training | Mixed/Half precision training |
Psudolabeling | Psudolabeling |