Deep Pyramidal Residual Networks with Separated Stochastic Depth
On general object recognition, Deep Convolutional Neural Networks (DCNNs) achieve high accuracy. In particular, ResNet and its improvements have broken the lowest error rate records. In this paper, we propose a method to successfully combine two ResNet improvements, ResDrop and PyramidNet. We confirmed that the proposed network outperformed the conventional methods; on CIFAR-100, the proposed network achieved an error rate of 16.18% in contrast to PiramidNet achieving that of 18.29% and ResNeXt 17.31%.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Convolution •
Global Average Pooling •
Grouped Convolution •
Kaiming Initialization •
Max Pooling •
Pyramidal Bottleneck Residual Unit •
Pyramidal Residual Unit •
PyramidNet •
ReLU •
Residual Block •
Residual Connection •
ResNet •
ResNeXt •
ResNeXt Block •
Zero-padded Shortcut Connection