1 code implementation • 25 Nov 2023 • Zhiqiang Gong, Xian Zhou, Wen Yao, Xiaohu Zheng, Ping Zhong
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
no code implementations • 28 Oct 2023 • Zhiqiang Gong, Xian Zhou, Wen Yao
Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification.
no code implementations • 28 Oct 2023 • Zhiqiang Gong, Xian Zhou, Wen Yao
Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification.
no code implementations • 14 Apr 2023 • Yanfang Lyu, Xiaoyu Zhao, Zhiqiang Gong, Xiao Kang, Wen Yao
Therefore, this work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator by jointing abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm.
1 code implementation • 23 Feb 2023 • Yunyang Zhang, Zhiqiang Gong, Xiaoyu Zhao, Wen Yao
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering.
1 code implementation • 20 Feb 2023 • Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Weien Zhou, Wen Yao, Yunyang Zhang
The MLP embedding is propitious to more sparse input, while the others benefit from spatial information preservation and perform better with the increase of observation data.
no code implementations • 17 Jan 2023 • Yunyang Zhang, Zhiqiang Gong, Weien Zhou, Xiaoyu Zhao, Xiaohu Zheng, Wen Yao
Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process.
no code implementations • 16 Jul 2022 • Jiahao Qi, Zhiqiang Gong, Xingyue Liu, Kangcheng Bin, Chen Chen, YongQian Li, Wei Xue, Yu Zhang, Ping Zhong
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community.
1 code implementation • 25 May 2022 • Zhiqiang Gong, Ping Zhong, Jiahao Qi, Panhe Hu
Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image.
no code implementations • 19 May 2022 • Yu Zhang, Zhiqiang Gong, Yichuang Zhang, YongQian Li, Kangcheng Bin, Jiahao Qi, Wei Xue, Ping Zhong
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples.
no code implementations • 10 Mar 2022 • Yunyang Zhang, Zhiqiang Gong, Xiaohu Zheng, Xiaoyu Zhao, Wen Yao
However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model.
1 code implementation • 8 Mar 2022 • Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Wen Yao, Yunyang Zhang, Xiaohu Zheng
This paper proposes a contrastive enhancement approach using latent prototypes to leverage latent classes and raise the utilization of similarity information between prototype and query features.
no code implementations • 14 Feb 2022 • Xiaohu Zheng, Wen Yao, Zhiqiang Gong, Yunyang Zhang, Xiaoya Zhang
To solve the above problem, this paper proposes an unsupervised method, i. e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing temperature field and quantifying the aleatoric uncertainty caused by data noise.
1 code implementation • 14 Feb 2022 • Xiaohu Zheng, Wen Yao, Zhiqiang Gong, Yunyang Zhang, Xiaoyu Zhao, Tingsong Jiang
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
no code implementations • 26 Jan 2022 • Xingwen Peng, Xingchen Li, Zhiqiang Gong, Xiaoyu Zhao, Wen Yao
To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation.
1 code implementation • 18 Jan 2022 • Xu Liu, Wei Peng, Zhiqiang Gong, Weien Zhou, Wen Yao
In this work, we develop a physics-informed neural network-based temperature field inversion (PINN-TFI) method to solve the TFI-HSS task and a coefficient matrix condition number based position selection of observations (CMCN-PSO) method to select optima positions of noise observations.
1 code implementation • 26 Sep 2021 • Xiaoyu Zhao, Zhiqiang Gong, Yunyang Zhang, Wen Yao, Xiaoqian Chen
As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exists in surrogate for thermal analysis and design.
1 code implementation • 15 Sep 2021 • Donghua Wang, Tingsong Jiang, Jialiang Sun, Weien Zhou, Xiaoya Zhang, Zhiqiang Gong, Wen Yao, Xiaoqian Chen
To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors.
2 code implementations • 17 Aug 2021 • Xiaoqian Chen, Zhiqiang Gong, Xiaoyu Zhao, Weien Zhou, Wen Yao
To overcome this problem, this work develops a machine learning modelling benchmark for TFR-HSS task.
no code implementations • 15 Jul 2021 • Yufeng Xia, Jun Zhang, Zhiqiang Gong, Tingsong Jiang, Wen Yao
Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test.
1 code implementation • 22 Jun 2021 • Zhiqiang Gong, Weien Zhou, Jun Zhang, Wei Peng, Wen Yao
To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
1 code implementation • 20 Mar 2021 • Xianqi Chen, Xiaoyu Zhao, Zhiqiang Gong, Jun Zhang, Weien Zhou, Xiaoqian Chen, Wen Yao
Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products.
1 code implementation • 28 Dec 2019 • Zhiqiang Gong, Ping Zhong, Weidong Hu
To overcome this problem, this work characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning.
1 code implementation • 24 Dec 2019 • Zhiqiang Gong, Weidong Hu, Xiaoyong Du, Ping Zhong, Panhe Hu
Deep learning methods have played a more and more important role in hyperspectral image classification.
no code implementations • 13 May 2019 • Zhiqiang Gong, Ping Zhong, Weidong Hu, Zixuan Xiao, Xuping Yin
In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image.
no code implementations • 18 Mar 2019 • Zhiqiang Gong, Ping Zhong, Weidong Hu, Fang Liu, Bingwei Hui
Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes.
no code implementations • 4 Jul 2018 • Zhiqiang Gong, Ping Zhong, Weidong Hu
Even though the diversity plays an important role in machine learning process, there is no systematical analysis of the diversification in machine learning system.