no code implementations • 31 Jan 2020 • Chuanguang Yang, Zhulin An, Xiaolong Hu, Hui Zhu, Yongjun Xu
Deep convolutional neural networks (CNN) always depend on wider receptive field (RF) and more complex non-linearity to achieve state-of-the-art performance, while suffering the increased difficult to interpret how relevant patches contribute the final prediction.
no code implementations • 19 Jan 2020 • Hui Zhu, Zhulin An, Kaiqiang Xu, Xiaolong Hu, Yongjun Xu
Existing approaches to improve the performances of convolutional neural networks by optimizing the local architectures or deepening the networks tend to increase the size of models significantly.
no code implementations • 20 Nov 2019 • Xiaolong Hu, Zhulin An, Chuanguang Yang, Hui Zhu, Kaiqaing Xu, Yongjun Xu
For VGG16 pre-trained on ImageNet, our method averagely gains 14. 29\% accuracy promotion for two-classes sub-tasks.
no code implementations • 4 Sep 2019 • Hui Zhu, Zhulin An, Chuanguang Yang, Xiaolong Hu, Kaiqiang Xu, Yongjun Xu
In this paper, we propose a method for efficient automatic architecture search which is special to the widths of networks instead of the connections of neural architecture.
1 code implementation • 26 Aug 2019 • Chuanguang Yang, Zhulin An, Hui Zhu, Xiaolong Hu, Kun Zhang, Kaiqiang Xu, Chao Li, Yongjun Xu
We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our SMG module, which is augmented by local residual.
Ranked #60 on Image Classification on CIFAR-10