Proceedings ArticleDOI
MatConvNet: Convolutional Neural Networks for MATLAB
Andrea Vedaldi,Karel Lenc +1 more
- pp 689-692
TLDR
MatConvNet exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more.Abstract:
MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more. MatConvNet can be easily extended, often using only MATLAB code, allowing fast prototyping of new CNN architectures. At the same time, it supports efficient computation on CPU and GPU, allowing to train complex models on large datasets such as ImageNet ILSVRC containing millions of training examplesread more
Citations
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Journal ArticleDOI
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
TL;DR: Zhang et al. as mentioned in this paper proposed a feed-forward denoising convolutional neural networks (DnCNNs) to handle Gaussian denobling with unknown noise level.
Proceedings ArticleDOI
Deep face recognition
TL;DR: It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
Proceedings ArticleDOI
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
TL;DR: DeepFool as discussed by the authors proposes the DeepFool algorithm to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers by making them more robust.
Proceedings ArticleDOI
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TL;DR: In this article, a very deep convolutional network inspired by VGG-net was used for image superresolution, which achieved state-of-the-art performance in accuracy.
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TL;DR: This work presents a highly accurate single-image superresolution (SR) method using a very deep convolutional network inspired by VGG-net used for ImageNet classification and uses extremely high learning rates enabled by adjustable gradient clipping.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
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Karen Simonyan,Andrew Zisserman +1 more
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Proceedings ArticleDOI
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Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
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Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
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