Search Results for author: Xiaolong Hu

Found 5 papers, 1 papers with code

Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task

no code implementations31 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.

General Classification Image Classification

Towards More Efficient and Effective Inference: The Joint Decision of Multi-Participants

no code implementations19 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.

DRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners

no code implementations20 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.

Rethinking the Number of Channels for the Convolutional Neural Network

no code implementations4 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.

Neural Architecture Search

Gated Convolutional Networks with Hybrid Connectivity for Image Classification

1 code implementation26 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.

Adversarial Defense General Classification +1

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