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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Exploiting Unintended Feature Leakage in Collaborative Learning
TL;DR: In this article, passive and active inference attacks are proposed to exploit the leakage of information about participants' training data in federated learning, where each participant can infer the presence of exact data points and properties that hold only for a subset of the training data and are independent of the properties of the joint model.
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Dynamic Few-Shot Visual Learning Without Forgetting
Spyros Gidaris,Nikos Komodakis +1 more
TL;DR: This work proposes to extend an object recognition system with an attention based few-shot classification weight generator, and to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors.
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Texture synthesis using convolutional neural networks
TL;DR: A new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition is introduced, showing that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit.
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Do Better ImageNet Models Transfer Better
TL;DR: In this article, the authors compare the performance of 16 classification networks on 12 image classification datasets and find that when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy.
Proceedings ArticleDOI
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
TL;DR: In this paper, a Fully Convolutional Localization Network (FCLN) is proposed to address the localization and description task jointly, which can be trained end-to-end with a single round of optimization.
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).