CutOut
- Date: 2017/08
- Paper: Improved Regularization of Convolutional Neural Networks with Cutout
MixUP
- Date: 2017/10
- Paper: mixup: Beyond Empirical Risk Minimization
CutMix
- Date: 2019/05
- Paper: CutMix: Regularization Strategy to Train Strong Classifierswith Localizable Features
GIoU
- Date: 2019/02
- Paper: Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
DIoU
- Date: 2019/11
- Paper: Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
VGG
- Date: 2014/09
- Paper: Very Deep Convolutional Networks for Large-Scale Image Recognition
- Code(keras):https://github.com/keras-team/keras-applications
ResNet
- Date: 2015/12
- Paper: Deep Residual Learning for Image Recognition
- Code(keras):https://github.com/keras-team/keras-applications
ResNetV2
- Date: 2016/03
- Paper: Identity Mappings in Deep Residual Networks
- Code(keras):https://github.com/keras-team/keras-applications
ResNeXt
- Date: 2016/11
- Paper: Aggregated Residual Transformations for Deep Neural Networks
- Code(keras):https://github.com/keras-team/keras-applications
InceptionV1
- Date: 2014/09
- Paper: Going Deeper with Convolutions
InceptionV2
- Date: 2015/02
- Paper: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
InceptionV3
- Date: 2015/12
- Paper: Rethinking the Inception Architecture for Computer Vision
- Code(keras):https://github.com/keras-team/keras-applications
InceptionV4/InceptionResNetV2
- Date: 2016/02
- Paper: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
- Code(keras):https://github.com/keras-team/keras-applications
DenseNet
- Date: 2016/08
- Paper: Densely Connected Convolutional Networks
- Code(keras):https://github.com/keras-team/keras-applications
Xecption
- Date: 2016/10
- Paper: Xception: Deep Learning with Depthwise Separable Convolutions
- Code(keras):https://github.com/keras-team/keras-applications
MobileNet
- Date: 2017/04
- Paper: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Code(keras):https://github.com/keras-team/keras-applications
NASNet
- Date: 2017/07
- Paper: Learning Transferable Architectures for Scalable Image Recognition
- Code(keras):https://github.com/keras-team/keras-applications
EfficientNet
- Date: 2019/05
- Paper: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- Code(keras):https://github.com/keras-team/keras-applications
RCNN
- Date: 2013/11
- Paper: Rich feature hierarchies for accurate object detection and semantic segmentation
Fast RCNN
- Date: 2015/04
- Paper: Fast R-CNN
Faster RCNN
- Date: 2015/06
- Paper: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
SSD
- Date: 2015/12
- Paper: SSD: Single Shot MultiBox Detector
YOLO_V1
- Date: 2015/06
- Paper: You Only Look Once: Unified, Real-Time Object Detection
YOLO_V2
- Date: 2016/12
- Paper: YOLO9000: Better, Faster, Stronger
YOLO_V3
- Date: 2018/04
- Paper: YOLOv3: An Incremental Improvement
- Code(darknet): https://github.com/pjreddie/darknet
YOLO_V4
- Date: 2020/04
- Paper: YOLOv4: Optimal Speed and Accuracy of Object Detection
- Code(darknet): https://github.com/AlexeyAB/darknet
RetinaNet
- Date: 2017/08
- Paper: Focal Loss for Dense Object Detection
CenterNet
- Date: 2019/04
- Paper: Objects as Points
MatrixNet
- Date: 2019/08
- Paper: Matrix Nets: A New Deep Architecture for Object Detection
UNet
- Date: 2015/05
- Paper: U-Net: Convolutional Networks for Biomedical Image Segmentation
SegNet
- Date: 2015/11
- Paper: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
FCN
- Date: 2014/12
- Paper: Fully Convolutional Networks for Semantic Segmentation
DeepLabV1
- Date: 2014/12
- Paper: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
DeepLabV2
- Date: 2016/06
- Paper: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
DeepLabV3
- Date: 2017/06
- Paper: Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLabV3+
- Date: 2018/02
- Paper: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
MaskRCNN
- Date: 2017/03
- Paper: Mask R-CNN
GAN
- Date: 2014/06
- Paper: Generative Adversarial Networks
DCGAN
- Date: 2015/11
- Paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
InfoGAN
- Date: 2016/06
- Paper: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
CycleGAN
- Date: 2017/03
- Paper: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Code(pytorch): https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
StyleGAN
- Date: 2018/12
- Paper: A Style-Based Generator Architecture for Generative Adversarial Networks
- Code(tensorflow): https://github.com/NVlabs/stylegan
SRGAN
- Date: 2016/09
- Paper: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
ESRGAN
- Date: 2019/09
- Paper: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks