no code implementations • 20 Jan 2022 • Haidong Xie, Jia Tan, Xiaoying Zhang, Nan Ji, Haihua Liao, Zuguo Yu, Xueshuang Xiang, Naijin Liu
This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform.
no code implementations • 16 Mar 2021 • Nan Ji, YanFei Feng, Haidong Xie, Xueshuang Xiang, Naijin Liu
To improve the ability of Ad-YOLO to detect variety patches, we first use an adversarial training process to develop a patch dataset based on the Inria dataset, which we name Inria-Patch.
no code implementations • 15 Mar 2021 • Wei Bao, Meiyu Huang, Yaqin Zhang, Yao Xu, Xuejiao Liu, Xueshuang Xiang
In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.
1 code implementation • 15 Mar 2021 • Meiyu Huang, Yao Xu, Lixin Qian, Weili Shi, Yaqin Zhang, Wei Bao, Nan Wang, Xuejiao Liu, Xueshuang Xiang
We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images.
no code implementations • 11 Jul 2020 • Xuejiao Liu, Xueshuang Xiang
Furthermore, if the labeled data can traverse all connected subdomains of the data manifold, which is reasonable in semi-supervised classification, we additionally expect the optimal discriminator in GAN-SSL to also be perfect on unlabeled data.
no code implementations • 10 Apr 2020 • Xuejiao Liu, Yao Xu, Xueshuang Xiang
Generative adversarial networks (GANs) have attracted intense interest in the field of generative models.
1 code implementation • 10 Apr 2020 • Haidong Xie, Xueshuang Xiang, Naijin Liu, Bin Dong
The main idea of this approach is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used in the training, ensuring that the strengths of the AEs are dynamically located in a reasonable range and ultimately improving the overall robustness of the AT model.
1 code implementation • 10 Apr 2020 • Haidong Xie, Lixin Qian, Xueshuang Xiang, Naijin Liu
Furthermore, to better balance the AER, we propose an approach called blind adversarial pruning (BAP), which introduces the idea of blind adversarial training into the gradual pruning process.
no code implementations • 10 Apr 2020 • Meiyu Huang, Xueshuang Xiang, Yao Xu
Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning.
no code implementations • 26 Jun 2019 • Yao Xu, Xueshuang Xiang, Meiyu Huang
The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.
no code implementations • 5 Dec 2018 • Haipeng Jia, Xueshuang Xiang, Da Fan, Meiyu Huang, Changhao Sun, Yang He
Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights.