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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Benchmark Analysis of Representative Deep Neural Network Architectures
TL;DR: An in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition, with a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future.
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Neural Network Methods in Natural Language Processing
Yoav Goldberg,Graeme Hirst +1 more
TL;DR: Neural networks are a family of powerful machine learning models as mentioned in this paper, and they have been widely used in natural language processing applications such as machine translation, syntactic parsing, and multi-task learning.
Proceedings ArticleDOI
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
TL;DR: Split-brain autoencoders as mentioned in this paper add a split to the network, resulting in two disjoint sub-networks, each sub-network is trained to perform a difficult task predicting one subset of the data channels from another.
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Moment Matching for Multi-Source Domain Adaptation
TL;DR: In this article, the authors proposed a new deep learning approach, Moment Matching for Multi-source Domain Adaptation M3SDA, which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions.
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BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
TL;DR: BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters’ gradient is introduced, which drastically reduces memory usage and replaces most multiplications by 1-bit exclusive-not-or (XNOR) operations, which might have a big impact on both general-purpose and dedicated Deep Learning hardware.
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).