• Corpus ID: 13439545

Analysis and Optimization of Convolutional Neural Network Architectures

@article{Thoma2017AnalysisAO,
  title={Analysis and Optimization of Convolutional Neural Network Architectures},
  author={Martin Thoma},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.09725}
}
Convolutional Neural Networks (CNNs) dominate various computer vision tasks since Alex Krizhevsky showed that they can be trained effectively and reduced the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual recognition challenge. [] Key Method A novel way to visualize classification errors with confusion matrices was developed. Based on this method, hierarchical classifiers are described and evaluated. Additionally, some results are confirmed and quantified for CIFAR-100. For example, the…

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