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ai_papers

★ 251 Open GitHub ↗

AI Papers

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## Data
**CutOut**
- *Date:* 2017/08
- *Paper:* [Improved Regularization of Convolutional Neural Networks with Cutout](https://arxiv.org/abs/1708.04552)

**MixUP**
- *Date:* 2017/10
- *Paper:* [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)

**CutMix**
- *Date:* 2019/05
- *Paper:* [CutMix: Regularization Strategy to Train Strong Classifierswith Localizable Features](https://arxiv.org/abs/1905.04899)

## Base
**GIoU**
- *Date:* 2019/02
- *Paper:* [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://arxiv.org/abs/1902.09630)

**DIoU**
- *Date:* 2019/11
- *Paper:* [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://arxiv.org/abs/1911.08287)

## Image Classification
**VGG**
- *Date:* 2014/09
- *Paper:* [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**ResNet**
- *Date:* 2015/12
- *Paper:* [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**ResNetV2**
- *Date:* 2016/03
- *Paper:* [Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**ResNeXt**
- *Date:* 2016/11
- *Paper:* [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**InceptionV1**
- *Date:* 2014/09
- *Paper:* [Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842)

**InceptionV2**
- *Date:* 2015/02
- *Paper:* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)

**InceptionV3**
- *Date:* 2015/12
- *Paper:* [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567)
- *Code(keras):*[https://github.com/keras-team/keras-applications](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](https://arxiv.org/abs/1602.07261)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**DenseNet**
- *Date:* 2016/08
- *Paper:* [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**Xecption**
- *Date:* 2016/10
- *Paper:* [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**MobileNet**
- *Date:* 2017/04
- *Paper:* [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**NASNet**
- *Date:* 2017/07
- *Paper:* [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

**EfficientNet**
- *Date:* 2019/05
- *Paper:* [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
- *Code(keras):*[https://github.com/keras-team/keras-applications](https://github.com/keras-team/keras-applications)

## Object Detection
**RCNN**
- *Date:* 2013/11
- *Paper:* [Rich feature hierarchies for accurate object detection and semantic segmentation](https://arxiv.org/abs/1311.2524)

**Fast RCNN**
- *Date:* 2015/04
- *Paper:* [Fast R-CNN](https://arxiv.org/abs/1504.08083)

**Faster RCNN**
- *Date:* 2015/06
- *Paper:* [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)

**SSD**
- *Date:* 2015/12
- *Paper:* [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325)

**YOLO_V1**
- *Date:* 2015/06
- *Paper:* [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640)

**YOLO_V2**
- *Date:* 2016/12
- *Paper:* [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242)

**YOLO_V3**
- *Date:* 2018/04
- *Paper:* [YOLOv3: An Incremental Improvement](https://arxiv.org/abs/1804.02767)
- *Code(darknet):* [https://github.com/pjreddie/darknet](https://github.com/pjreddie/darknet)

**YOLO_V4**
- *Date:* 2020/04
- *Paper:* [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/abs/2004.10934)
- *Code(darknet):* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)

**RetinaNet**
- *Date:* 2017/08
- *Paper:* [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)

**CenterNet**
- *Date:* 2019/04
- *Paper:* [Objects as Points](https://arxiv.org/abs/1904.07850)

**MatrixNet**
- *Date:* 2019/08
- *Paper:* [Matrix Nets: A New Deep Architecture for Object Detection](https://arxiv.org/abs/1908.04646)
## Semantic Segmentation

**UNet**
- *Date:* 2015/05
- *Paper:* [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)

**SegNet**
- *Date:* 2015/11
- *Paper:* [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://arxiv.org/abs/1511.00561)

**FCN**
- *Date:* 2014/12
- *Paper:* [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1605.06211)

**DeepLabV1**
- *Date:* 2014/12
- *Paper:* [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://arxiv.org/abs/1412.7062v3)

**DeepLabV2**
- *Date:* 2016/06
- *Paper:* [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)

**DeepLabV3**
- *Date:* 2017/06
- *Paper:* [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)

**DeepLabV3+**
- *Date:* 2018/02
- *Paper:* [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)

## Instance Segmentation
**MaskRCNN**
- *Date:* 2017/03
- *Paper:* [Mask R-CNN](https://arxiv.org/abs/1703.06870)

## GAN
**GAN**
- *Date:* 2014/06
- *Paper:* [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661)

**DCGAN**
- *Date:* 2015/11
- *Paper:* [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434)

**InfoGAN**
- *Date:* 2016/06
- *Paper:* [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://arxiv.org/abs/1606.03657)

**CycleGAN**
- *Date:* 2017/03
- *Paper:* [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)
- *Code(pytorch):* [https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)

**StyleGAN**
- *Date:* 2018/12
- *Paper:* [A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/abs/1812.04948)
- *Code(tensorflow):* [https://github.com/NVlabs/stylegan](https://github.com/NVlabs/stylegan)

**SRGAN**
- *Date:* 2016/09
- *Paper:* [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802)

**ESRGAN**
- *Date:* 2019/09
- *Paper:* [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://arxiv.org/abs/1809.00219)

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