Coffee Space

Deep Learning

The following papers are suggested from [1]:

• ImageNet Classification with Deep Convolution Neural Networks - (link)
• Visualizing and Understanding Convolution Networks - (link)
• Very Deep Convolution Networks for Large-Scale Image Recognitions - (link)
• Going Deeper with Convolutions - (link)
• Deep Residual Learning for Image Recognition - (link)
• Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation - (link)
• Fast R-CNN - (link)
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks - (link)
• Generative Adversarial Nets - (link)
• Deep Visual-Semantic Alignments for Generating Image Descriptions - (link)
• Spatial Transformer Networks - (link)

Resources

[1] https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html