Deep Learning Paper

Convolutional Neural Network Models

Rethinking the inception architecture for computer vision (2016), C. Szegedy et al. pdf
Inception-v4, inception-resnet and the impact of residual connections on learning (2016), C. Szegedy et al. pdf
Identity Mappings in Deep Residual Networks (2016), K. He et al. pdf
ResNet: Deep residual learning for image recognition (2016), K. He et al. pdf
Spatial transformer network (2015), M. Jaderberg et al., pdf
Going deeper with convolutions (2015), C. Szegedy et al. pdf
VGG: Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman pdf
Return of the devil in the details: delving deep into convolutional nets (2014), K. Chatfield et al. pdf
OverFeat: Integrated recognition, localization and detection using convolutional networks (2013), P. Sermanet et al. pdf
Maxout networks (2013), I. Goodfellow et al. pdf
NIN: Network in network (2013), M. Lin et al. pdf
AlexNet: ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. pdf

YOLOv3: An Incremental Improvement(2018). pdf
YOLO9000: Better, Faster, Stronger(2016). pdf
YOLO: You only look once: Unified, real-time object detection (2016), J. Redmon et al. pdf
U-Net: Convolutional Networks for Biomedical Image Segmentation. pdf
SSD: Single Shot MultiBox Detector. pdf
FCN: Fully convolutional networks for semantic segmentation (2015), J. Long et al. pdf
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. pdf
Fast R-CNN (2015), R. Girshick pdf
RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. pdf
Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. pdf
DeepLabv4:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(2018). pdf
DeepLabv3:Rethinking Atrous Convolution for Semantic Image Segmentation(2017). pdf
DeepLabv2: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs(2016). pdf
DeepLabv1: Semantic image segmentation with deep convolutional nets and fully connected CRFs(2014). pdf

Show and tell: A neural image caption generator, 2015 CVPRpdf
Show, attend and tell: Neural image caption generation with visual attention, 2015 ICMLpdf
VQA: Visual question answering (2015), S. Antol et al. pdf