Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TLDR
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.Abstract:
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.read more
Citations
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Proceedings ArticleDOI
Dilated Residual Networks
TL;DR: In this paper, dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the models depth or complexity, and an approach to remove gridding artifacts introduced by dilation is proposed.
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Channel Pruning for Accelerating Very Deep Neural Networks
Yihui He,Xiangyu Zhang,Jian Sun +2 more
TL;DR: In this article, a LASSO regression based channel selection and least square reconstruction is proposed to accelerate very deep convolutional neural networks, which reduces the accumulated error and enhances the compatibility with various architectures.
Journal ArticleDOI
When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs
TL;DR: This paper proposes a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification and comprehensively evaluates the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models.
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A Review on Deep Learning Techniques Applied to Semantic Segmentation.
Alberto Garcia-Garcia,Sergio Orts-Escolano,Sergiu Oprea,Victor Villena-Martinez,Jose Garcia-Rodriguez +4 more
TL;DR: A review on deep learning methods for semantic segmentation applied to various application areas as well as mandatory background concepts to help researchers decide which are the ones that best suit their needs and their targets.
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BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.
TL;DR: It is shown that outsourced training introduces new security risks: an adversary can create a maliciously trained network (a backdoored neural network, or a BadNet) that has state-of-the-art performance on the user's training and validation samples, but behaves badly on specific attacker-chosen inputs.
References
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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 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.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).