dhfromkorea / dl-papers

summary notes for deep learning papers

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neural turing machine alexnet [Krizhevsky et al. 2012] yolo attention show and tell LeNet-5 [LeCun et al., 1998] VGGNet [Simonyan and Zisserman, 2014] GoogLeNet [Szegedy et al., 2014] ResNet [He et al., 2015] R-CNN Fast R-CNN Faster R-CNN YOLO t-SNE visualization [van der Maaten & Hinton] Occlusion experiments [Zeiler & Fergus 2013] [Visualizing and Understanding Convolutional Networks, Zeiler and Fergus 2013] [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, Simonyan et al., 2014] [Striving for Simplicity: The all convolutional net, Springenberg, Dosovitskiy, et al., 2015] [ A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge, 2015] EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES [Goodfellow, Shlens & Szegedy, 2014]

[Delving Deeper into Convolutional Networks for Learning Video Representations, Ballas et al., 2016] Autoencoder Generative Adversarial Network

https://github.com/terryum/awesome-deep-learning-papersersonal summary notes for deep learning papers https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

##videos

  • Dense trajectories and motion boundary descriptors for action recognition Wang et al., 2013
  • Action Recognition with Improved Trajectories Wang and Schmid, 2013
  • 3D Convolutional Neural Networks for Human Action Recognition, Ji et al., 2010
  • Sequential Deep Learning for Human Action Recognition, Baccouche et al., 2011
  • Large-scale Video Classification with Convolutional Neural Networks, Karpathy et al., 2014
  • Learning Spatiotemporal Features with 3D Convolutional Networks, Tran et al. 2015
  • Two-Stream Convolutional Networks for Action Recognition in Videos, Simonyan and Zisserman 2014
  • T. Brox and J. Malik, “Large displacement optical flow: Descriptor matching in variational motion estimation,” 2011
  • Sequential Deep Learning for Human Action Recognition, Baccouche et al., 2011
  • Long-term Recurrent Convolutional Networks for Visual Recognition and Description, Donahue et al., 2015
  • Beyond Short Snippets: Deep Networks for Video Classification, Ng et al., 2015
  • Delving Deeper into Convolutional Networks for Learning Video Representations, Ballas et al., 2016

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summary notes for deep learning papers