CReLU (Concatenated Rectified Linear Units) with Caffe
CReLU (Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units)
By Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee
Paper : https://arxiv.org/pdf/1603.05201v2.pdf
If you use these models in your research, please cite:
@article{shang2016understanding,
title={Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units},
author={Shang, Wenling and Sohn, Kihyuk and Almeida, Diogo and Lee, Honglak},
journal={arXiv preprint arXiv:1603.05201},
year={2016}
}
Results
1-crop validation error on ImageNet (center 224x224 crop from resized image with shorter side=256):
model | top-1 | top-5 |
---|---|---|
AlexNet | 42.8% | 19.7% |
SqueezeNet | 42.5% | 19.7% |
CReLU (all) in paper | 40.93% | 19.39% |
CReLU (conv1,4,7) in paper | 40.45 % | 18.58% |
CReLU (conv1–4) in paper | 39.82% | 18.28% |
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