1 code implementation • 13 May 2023 • Jun Shu, Xiang Yuan, Deyu Meng, Zongben Xu
Besides, meta-data-driven meta-loss objective combined with DAC-MR is capable of achieving better meta-level generalization.
1 code implementation • 23 Mar 2023 • Xiang Gu, Yucheng Yang, Wei Zeng, Jian Sun, Zongben Xu
In this paper, we propose a novel KeyPoint-Guided model by ReLation preservation (KPG-RL) that searches for the optimal matching (i. e., transport plan) guided by the keypoints in OT.
no code implementations • 18 Jan 2023 • Kehui Ding, Jun Shu, Deyu Meng, Zongben Xu
To achieve setting such instance-dependent hyperparameters for robust loss, we propose a meta-learning method capable of adaptively learning a hyperparameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster).
no code implementations • 28 Nov 2022 • Zai Yang, Yi-Lin Mo, Gongguo Tang, Zongben Xu
Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises.
1 code implementation • 21 Sep 2022 • Jiahong Fu, Hong Wang, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images.
1 code implementation • 11 Feb 2022 • Jun Shu, Xiang Yuan, Deyu Meng, Zongben Xu
Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics.
Ranked #3 on Image Classification on WebVision-1000
1 code implementation • NeurIPS 2021 • Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on Partial Domain Adaptation on ImageNet-Caltech
no code implementations • 24 Nov 2021 • Yuwen Yang, Feifei Gao, Jiang Xue, Ting Zhou, Zongben Xu
In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems.
1 code implementation • 30 Jul 2021 • Qi Xie, Qian Zhao, Zongben Xu, Deyu Meng
It has been shown that equivariant convolution is very helpful for many types of computer vision tasks.
1 code implementation • 26 Jul 2021 • Heran Yang, Jian Sun, Liwei Yang, Zongben Xu
Hyper-GAN consists of a pair of hyper-encoder and hyper-decoder to first map from the source contrast to a common feature space, and then further map to the target contrast image.
no code implementations • 6 Jul 2021 • Jun Shu, Deyu Meng, Zongben Xu
Meta learning has attracted much attention recently in machine learning community.
no code implementations • 11 May 2021 • Qiang Hu, Feifei Gao, Hao Zhang, Geoffrey Y. Li, Zongben Xu
We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels.
1 code implementation • CVPR 2021 • Shipeng Wang, Xiaorong Li, Jian Sun, Zongben Xu
To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameters in the null space of previous tasks.
no code implementations • 6 Feb 2021 • Jianyong Sun, Xin Liu, Thomas Bäck, Zongben Xu
A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i. e. parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure.
no code implementations • 21 Jan 2021 • Xunmeng Wu, Zai Yang, Zongben Xu
This paper investigates the recovery of a spectrally sparse signal from its partially revealed noisy entries within the framework of spectral compressive sensing.
Compressive Sensing Matrix Completion Information Theory Information Theory
no code implementations • 1 Jan 2021 • Xiang Gu, Jiasun Feng, Jian Sun, Zongben Xu
In this framework, we model the domain generalization as a learning problem that enforces the learner to be able to generalize well for any train/val subsets splitting of the training dataset.
1 code implementation • 3 Nov 2020 • Haotian Zhang, Yuhao Wang, Jianyong Sun, Zongben Xu
Efficient exploration is one of the most important issues in deep reinforcement learning.
no code implementations • 2 Sep 2020 • Ziyi Yang, Jun Shu, Yong Liang, Deyu Meng, Zongben Xu
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data.
no code implementations • 5 Aug 2020 • Haixia Bi, Lin Xu, Xiangyong Cao, Yong Xue, Zongben Xu
Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications.
no code implementations • 29 Jul 2020 • Jun Shu, Yanwen Zhu, Qian Zhao, Zongben Xu, Deyu Meng
Meanwhile, it always needs to search proper LR schedules from scratch for new tasks, which, however, are often largely different with task variations, like data modalities, network architectures, or training data capacities.
1 code implementation • ECCV 2020 • Jing Yao, Danfeng Hong, Jocelyn Chanussot, Deyu Meng, Xiaoxiang Zhu, Zongben Xu
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR).
no code implementations • 10 Jun 2020 • Jun Shu, Qian Zhao, Zongben Xu, Deyu Meng
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted labels.
no code implementations • 6 Jun 2020 • Jianyong Sun, Wei Zheng, Qingfu Zhang, Zongben Xu
Based on the new encoding method and the two objectives, a multiobjective evolutionary algorithm (MOEA) based upon NSGA-II, termed as continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables.
no code implementations • 10 Mar 2020 • Haotian Zhang, Jianyong Sun, Zongben Xu
This paper proposes to learn a two-phase (including a minimization phase and an escaping phase) global optimization algorithm for smooth non-convex functions.
no code implementations • 4 Mar 2020 • Haotian Zhang, Jianyong Sun, Zongben Xu
This paper proposes the first-ever algorithmic framework for tuning hyper-parameters of stochastic optimization algorithm based on reinforcement learning.
no code implementations • 2 Mar 2020 • Haotian Zhang, Jianyong Sun, Zongben Xu
Tuning hyper-parameters for evolutionary algorithms is an important issue in computational intelligence.
no code implementations • 16 Feb 2020 • Jun Shu, Qian Zhao, Keyu Chen, Zongben Xu, Deyu Meng
Four kinds of SOTA robust loss functions are attempted to be integrated into our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its accuracy and generalization performance, as compared with conventional hyperparameter tuning strategy, even with carefully tuned hyperparameters.
no code implementations • ICLR 2020 • Haotian Zhang, Jian Sun, Zongben Xu
Bayesian optimization is an effective tool to optimize black-box functions and popular for hyper-parameter tuning in machine learning.
no code implementations • NeurIPS 2019 • Jian Sun, Zongben Xu
To compute high-resolution diffusion distance or segmentation mask, we design an up-sampling strategy by feature-attentional interpolation which can be learned when training spec-diff-net.
Image Segmentation Weakly supervised Semantic Segmentation +1
3 code implementations • NeurIPS 2019 • Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng
Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance.
Ranked #22 on Image Classification on Clothing1M (using extra training data)
no code implementations • CVPR 2019 • Qi Xie, Minghao Zhou, Qian Zhao, Deyu Meng, WangMeng Zuo, Zongben Xu
In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image.
2 code implementations • 22 Nov 2018 • Shipeng Wang, Jian Sun, Zongben Xu
Deep neural networks are traditionally trained using human-designed stochastic optimization algorithms, such as SGD and Adam.
no code implementations • 17 Sep 2018 • Hengtao He, Shi Jin, Chao-Kai Wen, Feifei Gao, Geoffrey Ye Li, Zongben Xu
Intelligent communication is gradually considered as the mainstream direction in future wireless communications.
no code implementations • 12 Sep 2018 • Heran Yang, Jian Sun, Aaron Carass, Can Zhao, Junghoon Lee, Zongben Xu, Jerry Prince
The cycleGAN is becoming an influential method in medical image synthesis.
no code implementations • 14 Aug 2018 • Jun Shu, Zongben Xu, Deyu Meng
This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.
1 code implementation • CVPR 2019 • Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, Ying Wu
However, previous deep learning methods need to pre-collect a large set of image pairs with/without synthesized rain for training, which tends to make the neural network be biased toward learning the specific patterns of the synthesized rain, while be less able to generalize to real test samples whose rain types differ from those in the training data.
Ranked #8 on Single Image Deraining on Test1200
no code implementations • 22 Mar 2018 • Jian Fang, Shao-Bo Lin, Zongben Xu
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples.
no code implementations • ICCV 2017 • Wei Wei, Lixuan Yi, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu
Videos taken in the wild sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks.
no code implementations • 27 Sep 2017 • Heran Yang, Jian Sun, Huibin Li, Lisheng Wang, Zongben Xu
There are two major challenges in this category of methods, i. e., atlas selection and label fusion.
no code implementations • 20 Jun 2017 • Kaidong Wang, Yao Wang, Qian Zhao, Deyu Meng, Zongben Xu
Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers.
no code implementations • 19 May 2017 • Yan Yang, Jian Sun, Huibin Li, Zongben Xu
Due to the combination of the advantages in model-based approach and deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction accuracies with fast computational speed.
1 code implementation • 1 May 2017 • Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, John Paisley
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.
Ranked #13 on Hyperspectral Image Classification on Indian Pines (Overall Accuracy metric)
no code implementations • 1 Feb 2017 • Yang Chen, Xiangyong Cao, Qian Zhao, Deyu Meng, Zongben Xu
In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i. i. d.
no code implementations • NeurIPS 2016 • Yan Yang, Jian Sun, Huibin Li, Zongben Xu
Compressive Sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI).
no code implementations • CVPR 2016 • Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu, Shuhang Gu, WangMeng Zuo, Lei Zhang
Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks.
no code implementations • 20 Apr 2016 • Lin Xu, Shao-Bo Lin, Jinshan Zeng, Xia Liu, Zongben Xu
In this paper, we find that SGD is not the unique greedy criterion and introduce a new greedy criterion, called "$\delta$-greedy threshold" for learning.
no code implementations • ICCV 2015 • Xiangyong Cao, Qian Zhao, Deyu Meng, Yang Chen, Zongben Xu
Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data.
no code implementations • ICCV 2015 • Qian Zhao, Deyu Meng, Xu Kong, Qi Xie, Wenfei Cao, Yao Wang, Zongben Xu
In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor.
no code implementations • 10 Nov 2015 • Huibin Li, Jian Sun, Dong Wang, Zongben Xu, Liming Chen
In this paper, we present a novel approach to automatic 3D Facial Expression Recognition (FER) based on deep representation of facial 3D geometric and 2D photometric attributes.
no code implementations • 17 May 2015 • Lin Xu, Shao-Bo Lin, Yao Wang, Zongben Xu
Re-scale boosting (RBoosting) is a variant of boosting which can essentially improve the generalization performance of boosting learning.
no code implementations • 7 Mar 2015 • Shaobo Lin, Xingping Sun, Zongben Xu, Jinshan Zeng
On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary.
no code implementations • 6 Mar 2015 • Wenfei Cao, Yao Wang, Jian Sun, Deyu Meng, Can Yang, Andrzej Cichocki, Zongben Xu
In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework.
no code implementations • CVPR 2015 • Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce
In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image.
no code implementations • 10 Feb 2015 • Zhi Han, Zongben Xu, Song-Chun Zhu
This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving primitives to explicitly represent moving corners, lines, feature points, etc., ii) FRAME /MRF model reproducing feature statistics extracted from input video to implicitly represent textured motion, such as water and fire.
no code implementations • 13 Nov 2014 • Lin Xu, Shaobo Lin, Jinshan Zeng, Zongben Xu
Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal projecting the target function to the space spanned by the selected atoms in each greedy step.
no code implementations • 18 Sep 2014 • Jian Fang, Shao-Bo Lin, Zongben Xu
We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning.
no code implementations • CVPR 2014 • Yi Peng, Deyu Meng, Zongben Xu, Chenqiang Gao, Yi Yang, Biao Zhang
As compared to the conventional RGB or gray-scale images, multispectral images (MSI) can deliver more faithful representation for real scenes, and enhance the performance of many computer vision tasks.
no code implementations • 23 May 2014 • Qi Xie, Deyu Meng, Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng, Zongben Xu
Nevertheless, so far the global optimal solution of WNNM problem is not completely solved yet due to its non-convexity in general cases.
no code implementations • 31 Mar 2014 • Xiangyu Chang, Yu Wang, Rongjian Li, Zongben Xu
Nevertheless, this framework has two serious drawbacks: One is that the solution of the framework unavoidably involves a considerable portion of redundant noise features in many situations, and the other is that the framework neither offers intuitive explanations on why this framework can select relevant features nor leads to any theoretical guarantee for feature selection consistency.
no code implementations • 9 Mar 2014 • Jian Yu, Zongben Xu
Cluster analysis has attracted more and more attention in the field of machine learning and data mining.
no code implementations • 24 Jan 2014 • Shaobo Lin, Xia Liu, Jian Fang, Zongben Xu
On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning.
no code implementations • 19 Dec 2013 • Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use.
no code implementations • 27 Oct 2013 • Jian Fang, Zongben Xu, Bingchen Zhang, Wen Hong, Yirong Wu
Multilook processing is a widely used speckle reduction approach in synthetic aperture radar (SAR) imaging.
no code implementations • journal 2010 • Zongben Xu, Jian Sun
The patch with larger structure sparsity will be assigned higher priority for further inpainting.