paper_study

스터디 진도표

• ImageNet Classification with Deep Convolutional Neural Networks(2012) [AlexNet]
• Very deep convolutional networks for large-scale image recognition (2014)[VGGNet]
• Going deeper with convolutions (2014)[GoogLeNet]
• Visualizing and understanding convolutional networks(2013)[ZFnet]
• Network in Network (2014)
• Delving Deep into Rectifiers: surpassing human-level performance on ImageNet classification (2015) / PR020 video
• Rethinking the Inception Architecture for Computer vision
• Batch Normalization (2015) / PR-021 video
• Deep residual learning for image recognition (2015) [ResNet] / PR-170 video
• Identity mappings in Deep residual networks (2016)
• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (2016)
• Densely Connected Convolutional Networks (2017) / PR-028 video
• MobileNets: efficient convolutional neural networks for mobile vision applications (2017) / PR-044 video
• DeCAF: A deep convolutional activation feature for generic visual recognition (2013)
• Rich feature hierarchies for accurate object detection and semantic segmentation
• Fully Convolutional Networks for Semantic Segmentation
• Learning Deconvolution Network for Semantic Segmentation
• Fast R-CNN (2015)
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks / PR-012 video
• SSD: Single-shot multibox detector / PR-132 video
• You Only Look Once / PR-016 video
• mask R-CNN / PR-057 video
• Focal loss for dense object detection
• Long Short Term Memory
• Sequence to Sequence learning with Neural networks (2014)
• Learning phrase representations using rnn encoder-decoder for statistical machine translation / PR-003 video
• Long-term recurrent convolutional networks for visual recognition and description
• Recurrent models of visual attention
• show , attend and tell: Neural Image caption generation with visual attention
• show and tell: a neural image caption generator / PR-041 video
• Attention is all you need / PR-049 video