Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the...
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| Veröffentlicht in: | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 1 - 9 |
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| Format: | Tagungsbericht Journal Article |
| Sprache: | Englisch |
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IEEE
01.06.2015
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| ISSN: | 1063-6919, 1063-6919 |
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| Abstract | We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. |
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| AbstractList | We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. |
| Author | Yangqing Jia Rabinovich, Andrew Reed, Scott Erhan, Dumitru Vanhoucke, Vincent Sermanet, Pierre Anguelov, Dragomir Szegedy, Christian Wei Liu |
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| SubjectTerms | Architecture (computers) Classification Computation Computer architecture Computer vision Conferences Constants Convolutional codes Networks Neural networks Object detection Pattern recognition Sparse matrices Visualization |
| Title | Going deeper with convolutions |
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