Open AccessProceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- Vol. 25, pp 1097-1105
TLDR
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.Abstract:
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, 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. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.read more
Citations
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Visual Place Recognition: A Survey
Stephanie Lowry,Niko Sünderhauf,Paul Newman,John J. Leonard,David D. Cox,Peter Corke,Michael Milford +6 more
TL;DR: A survey of the visual place recognition research landscape is presented, introducing the concepts behind place recognition, how a “place” is defined in a robotics context, and the major components of a place recognition system.
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MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
TL;DR: A detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches, shows that the hybrid Recurrent Neural Networkbased approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on this dataset.
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CNN-RNN: A Unified Framework for Multi-label Image Classification
TL;DR: In this article, a CNN-RNN framework is proposed to learn a joint image-label embedding to characterize the semantic label dependency as well as the image label relevance, and it can be trained end-to-end from scratch to integrate both information in a unified framework.
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Target Classification Using the Deep Convolutional Networks for SAR Images
TL;DR: A new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used, which can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.
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Deep Layer Aggregation
TL;DR: Deep layer aggregation as mentioned in this paper iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters, and experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.
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Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.