Merge pull request #33 from cNikolaou/patch-1

Improve the "Understanding CNN" section

README.md

@@ -216,17 +216,11 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ![understanding](https://cloud.githubusercontent.com/assets/5226447/8452083/1aaa0066-2023-11e5-800b-2248ead51584.PNG)
 (from Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.)
 
-* Equivariance and Equivalence of Representations [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf)
-* Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015.
-* Deep Neural Networks Are Easily Fooled [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf)
-* Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015.
-* Understanding Deep Image Representations by Inverting Them [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf)
-* Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.
-* Object Detectors Emerge in Deep Scene CNNs [[Paper]](http://arxiv.org/abs/1412.6856)
-* Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015.
-* Inverting Convolutional Networks with Convolutional Networks
-* Alexey Dosovitskiy, Thomas Brox, Inverting Convolutional Networks with Convolutional Networks, arXiv, 2015. [[Paper]](http://arxiv.org/abs/1506.02753)
-* Visualizing and Understanding CNN
+* Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf)
+* Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf) 
+* Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf)
+* Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015. [[arXiv Paper]](http://arxiv.org/abs/1412.6856)
+* Alexey Dosovitskiy, Thomas Brox, Inverting Visual Representations with Convolutional Networks, arXiv, 2015. [[Paper]](http://arxiv.org/abs/1506.02753)
 * Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014. [[Paper]](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)