398 citations · 2k across the 69 of their papers we have counts for
70 papers
The solution to Kadison's problem on orthonormal bases of unitaries for type factors
Yixin He, Quanyu Tang, Zongben Xu +1
In 1967, Kadison asked whether every type factor admits an orthonormal basis, with respect to its trace, consisting of unitaries. We resolve this problem in full ge…
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images
Cuican Yu, Guansong Lu, Yihan Zeng +7
Generating 3D faces from textual descriptions has a multitude of applications, such as gaming, movie, and robotics. Recent progresses have demonstrated the success of unconditional…
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Meta learning recently has been heavily researched and helped advance the contemporary machine learning. However, achieving well-performing meta-learning model requires a large amo…
Keypoint-Guided Optimal Transport: Models, Algorithms, and Applications
Xiang Gu, Yucheng Yang, Wei Zeng +2
Existing Optimal Transport (OT) methods mainly derive the optimal transport plan/matching under the criterion of transport cost/distance minimization, which may cause incorrect mat…
Improve Noise Tolerance of Robust Loss via Noise-Awareness
Kehui Ding, Jun Shu, Deyu Meng +1
Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current approaches for designing robust losses involve the introduction of noi…
Separation-Free Spectral Super-Resolution via Convex Optimization
Zai Yang, Yi-Lin Mo, Gongguo Tang +1
Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these…
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Jiahong Fu, Hong Wang, Qi Xie +3
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 co…
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate t…
Federated Dynamic Neural Network for Deep MIMO Detection
Yuwen Yang, Feifei Gao, Jiang Xue +2
In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detecto…
A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation
Heran Yang, Jian Sun, Liwei Yang +1
Cross-contrast image translation is an important task for completing missing contrasts in clinical diagnosis. However, most existing methods learn separate translator for each pair…
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
Qi Xie, Qian Zhao, Zongben Xu +1
It has been shown that equivariant convolution is very helpful for many types of computer vision tasks. Recently, the 2D filter parametrization technique plays an important role wh…
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
Jun Shu, Deyu Meng, Zongben Xu
Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict la…
Understanding Deep MIMO Detection
Qiang Hu, Feifei Gao, Hao Zhang +2
Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most DL-…
Training Networks in Null Space of Feature Covariance for Continual Learning
Shipeng Wang, Xiaorong Li, Jian Sun +1
In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in conti…
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
Jianyong Sun, Xin Liu, Thomas Bäck +1
Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorit…
Robust spectral compressive sensing via vanilla gradient descent
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. Nonconvex optimiz…
Amortized Variational Deep Q Network
Haotian Zhang, Yuhao Wang, Jianyong Sun +1
Efficient exploration is one of the most important issues in deep reinforcement learning. To address this issue, recent methods consider the value function parameters as random var…
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Ziyi Yang, Jun Shu, Yong Liang +2
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 ve…
Polarimetric SAR Image Semantic Segmentation with 3D Discrete Wavelet Transform and Markov Random Field
Haixia Bi, Lin Xu, Xiangyong Cao +2
Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging…
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
Jing Yao, Danfeng Hong, Jocelyn Chanussot +3
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR). Yet the development of unsupervised deep networks remai…
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Jun Shu, Yanwen Zhu, Qian Zhao +2
The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-d…
Graph Neural Network Encoding for Community Detection in Attribute Networks
Jianyong Sun, Wei Zheng, Qingfu Zhang +1
In this paper, we first propose a graph neural network encoding method for multiobjective evolutionary algorithm to handle the community detection problem in complex attribute netw…
Learning to be Global Optimizer
Haotian Zhang, Jianyong Sun, Zongben Xu
The advancement of artificial intelligence has cast a new light on the development of optimization algorithm. This paper proposes to learn a two-phase (including a minimization pha…
On Hyper-parameter Tuning for Stochastic Optimization Algorithms
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. Hyper-parameters impose s…