Removed duplicate entry

README.md

@@ -184,7 +184,6 @@ Okay, signal processing might not be directly related to deep learning, but stud
 - [ImageNet Classification with Deep Convolutional Neural Networks](http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf) - AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function.
 - [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901v3.pdf) - For the "deconvnet layer".
 - [Fast and Accurate Deep Network Learning by Exponential Linear Units](https://arxiv.org/pdf/1511.07289v1.pdf) - ELU activation function for CIFAR vision tasks.
-- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) - U-Net, an interesting approach to image segmentation, to me that slightly resembles a convolutional autoencoder but with proper pixel-to-pixel alignment of segmentation.
 - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556v6.pdf) - Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".
 - [Going Deeper with Convolutions](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) - GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.
 - [Highway Networks](https://arxiv.org/pdf/1505.00387v2.pdf) - Highway networks: residual connections.