Book ChapterDOI
Convolutional Neural Networks
Nikhil Ketkar
- pp 63-78
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TLDR
Convolution Neural Networks (CNNs) in essence are neural networks that employ the convolution operation (instead of a fully connected layer) as one of its layers.Abstract:
Convolution Neural Networks (CNNs) in essence are neural networks that employ the convolution operation (instead of a fully connected layer) as one of its layers.read more
Citations
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Proceedings ArticleDOI
DeepTest: automated testing of deep-neural-network-driven autonomous cars
TL;DR: DeepTest is a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes and systematically explore different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons.
Proceedings ArticleDOI
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Jason Wei,Kai Zou +1 more
TL;DR: This paper proposed easy data augmentation techniques for boosting performance on text classification tasks, which consists of synonym replacement, random insertion, random swap, and random deletion, and showed that EDA improves performance for both convolutional and recurrent neural networks.
Proceedings ArticleDOI
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
TL;DR: The authors proposed a simple and effective approach to incorporate syntactic structure into neural attention-based encoder-decoder models for machine translation by using graph convolutional networks (GCNs).
Proceedings Article
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
TL;DR: One of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks and also utilizes 2D convolution to sample more meaningful information of the matrix.
Journal ArticleDOI
An object-based convolutional neural network (OCNN) for urban land use classification
TL;DR: The proposed OCNN framework is the first object-based convolutional neural network framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images, and was tested on aerial photography of two large urban scenes in Southampton and Manchester.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.