Adventures in deep learning

转:https://github.com/GKalliatakis/Adventures-in-deep-learning

Adventures in deep learning

State-of-the-art Deep Learning publications, frameworks & resources

Overview

Deep convolutional neural networks have led to a series of breakthroughs in large-scale image and video recognition. This repository aims at presenting an elaborate list of the latest works on the field of Deep Learning since 2013.

This is going to be an evolving repository and I will keep updating it (at least twice monthly).


State-of-the-art papers (Descending order based on Google Scholar Citations)

  1. Very deep convolutional networks for large-scale image recognition (VGG-net) (2014) [pdf] [video]
  2. Going deeper with convolutions (GoogLeNet) by Google (2015) [pdf] [video]
  3. Deep learning (2015) [pdf]
  4. Visualizing and Understanding Convolutional Neural Networks (ZF Net) (2014) [pdf] [video]
  5. Fully convolutional networks for semantic segmentation (2015) [pdf]
  6. Deep residual learning for image recognition (ResNet) by Microsoft (2015) [pdf] [video]
  7. Deepface: closing the gap to human-level performance in face verification (2014) [pdf] [video]
  8. Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) [pdf]
  9. Deep Learning in Neural Networks: An Overview (2015) [pdf]
  10. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (PReLU) (2014) [pdf]
  11. Faster R-CNN: Towards real-time object detection with region proposal networks (2015) [pdf]
  12. Fast R-CNN (2015) [pdf]
  13. Spatial pyramid pooling in deep convolutional networks for visual recognition (SPP Net) (2014) [pdf] [video]
  14. Generative Adversarial Nets (2014) [pdf]
  15. Spatial Transformer Networks (2015) [pdf] [video]
  16. Understanding deep image representations by inverting them (2015) [pdf]
  17. Deep Learning of Representations: Looking Forward (2013) [pdf]

Classic publications

  • ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) (2012) [pdf]
  • Rectified linear units improve restricted boltzmann machines (ReLU) (2010) [pdf]

Theory

  1. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images (2015) [pdf]
  2. Distilling the Knowledge in a Neural Network (2015) [pdf]
  3. Deep learning in neural networks: An overview (2015) [pdf]

Books

  • Deep Learning Textbook - An MIT Press book (2016) [html]
  • Learning Deep Architectures for AI [pdf]
  • Neural Nets and Deep Learning [html] [github]

Courses / Tutorials (Webpages unless other is stated)


Resources / Models (GitHub repositories unless other is stated)


Frameworks & Libraries (Descending order based on GitHub stars)

posted on   暖风的风  阅读(268)  评论(0编辑  收藏  举报

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