Deep Pyramidal Residual Networks

@article{Han2017DeepPR,
  title={Deep Pyramidal Residual Networks},
  author={Dongyoon Han and Jiwhan Kim and Junmo Kim},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={6307-6315}
}
Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. Generally, deep neural network architectures are stacks consisting of a large number of convolutional layers, and they perform downsampling along the spatial dimension via pooling to reduce memory usage. Concurrently, the feature map dimension (i.e., the number of channels) is sharply increased at downsampling locations, which is essential to ensure effective performance… Expand
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