no code implementations • Findings (EMNLP) 2021 • Tao Huang, Hong Chen
To improve the privacy guarantee and efficiency, we combine a subsampling method with CGS and propose a novel LDA training algorithm with differential privacy, SUB-LDA.
no code implementations • 20 Nov 2023 • Yuan Zhang, Tao Huang, Jiaming Liu, Tao Jiang, Kuan Cheng, Shanghang Zhang
(2) During the distillation period, a pixel-wise frequency mask is generated via Frequency Prompt, to localize those pixel of interests (PoIs) in various frequency bands.
no code implementations • 24 Oct 2023 • Hao Li, Quanwei Liu, Jianan Liu, Xiling Liu, Yanni Dong, Tao Huang, Zhihan Lv
High-resolution (HR) magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy in clinical settings.
no code implementations • 5 Oct 2023 • Tao Huang, Jianan Liu, Xi Zhou, Dinh C. Nguyen, Mostafa Rahimi Azghadi, Yuxuan Xia, Qing-Long Han, Sumei Sun
To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies.
1 code implementation • 18 Sep 2023 • Ting Meng, Chunyun Fu, Mingguang Huang, Xiyang Wang, JiaWei He, Tao Huang, Wankai Shi
However, in terms of the detection confidence fusing classification and localization, objects of low detection confidence may have inaccurate localization but clear appearance; similarly, objects of high detection confidence may have inaccurate localization or unclear appearance; yet these objects are not further classified.
no code implementations • 12 Sep 2023 • Jianan Liu, Guanhua Ding, Yuxuan Xia, Jinping Sun, Tao Huang, Lihua Xie, Bing Zhu
These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT.
no code implementations • 12 Sep 2023 • Hao Li, Yusheng Zhou, Jianan Liu, Xiling Liu, Tao Huang, Zhihan Lv
In this paper, we propose a novel MRI PI reconstruction method based on INR, which represents the reconstructed fully-sampled images as the function of voxel coordinates and prior feature vectors of undersampled images to overcome the generalization problem of INR.
no code implementations • 19 Aug 2023 • Zhenrong Zhang, Jianan Liu, Yuxuan Xia, Tao Huang, Qing-Long Han, Hongbin Liu
The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role.
1 code implementation • 31 Jul 2023 • Tao Huang, Kai Chen, Wang Wei, Jianan Li, Yonghao Long, Qi Dou
Based on this value function, a chaining policy is learned to instruct subtask policies to terminate at the state with the highest value so that all subsequent policies are more likely to be connected for accomplishing the task.
no code implementations • 20 Jul 2023 • Jianan Liu, Qiuchi Zhao, Weiyi Xiong, Tao Huang, Qing-Long Han, Bing Zhu
Additionally, KDE helps alleviate point cloud sparsity by capturing density features.
no code implementations • 5 Jul 2023 • Yiyao Zhou, Qianggang Wang, Yuan Chi, Jianquan Liao, Tao Huang, Niancheng Zhou, Xiaolong Xu, Xuefei Zhang
Optimal power flow (OPF) is a fundamental tool for analyzing the characteristics of bipolar DC distribution network (DCDN).
no code implementations • 3 Jul 2023 • Weiyi Xiong, Jianan Liu, Tao Huang, Qing-Long Han, Yuxuan Xia, Bing Zhu
They are sent to the core of LXL, called "radar occupancy-assisted depth-based sampling", to aid image view transformation.
no code implementations • 28 Jun 2023 • Zhixiang Yuan, Kaixin Zhang, Tao Huang
Multi-label classification (MLC) suffers from the inevitable label noise in training data due to the difficulty in annotating various semantic labels in each image.
no code implementations • 13 Jun 2023 • Tao Huang, Peder Bacher, Jan Kloppenborg Møller, Francesco D'Ettorre, Wiebke Brix Markussen
In addition, the model for local evaporator temperature can effectively adapt to different operational patterns and provide insight into the local cooling supply status.
1 code implementation • NeurIPS 2023 • Tao Huang, Yuan Zhang, Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Chang Xu
To address this, we propose to denoise student features using a diffusion model trained by teacher features.
1 code implementation • 4 May 2023 • Yuan Zhang, Weihua Chen, Yichen Lu, Tao Huang, Xiuyu Sun, Jian Cao
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again.
2 code implementations • 20 Feb 2023 • Tao Huang, Kai Chen, Bin Li, Yun-hui Liu, Qi Dou
Task automation of surgical robot has the potentials to improve surgical efficiency.
no code implementations • 4 Jan 2023 • Yusheng Zhou, Hao Li, Jianan Liu, Zhengmin Kong, Tao Huang, Euijoon Ahn, Zhihan Lv, Jinman Kim, David Dagan Feng
Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.
1 code implementation • ICCV 2023 • Yunlong Liu, Tao Huang, Weisheng Dong, Fangfang Wu, Xin Li, Guangming Shi
Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods.
1 code implementation • 1 Jan 2023 • Yonghao Long, Wang Wei, Tao Huang, Yuehao Wang, Qi Dou
We showcase the improvement of our simulation environment with the designed new features, and validate effectiveness of incorporating human factors in embodied intelligence through the use of human demonstrations and reinforcement learning as a representative example.
no code implementations • 11 Nov 2022 • Yanlong Yang, Jianan Liu, Tao Huang, Qing-Long Han, Gang Ma, Bing Zhu
Recent state-of-the-art works reveal that fusion of radar and LiDAR can lead to robust detection in adverse weather.
no code implementations • 24 Sep 2022 • Zhengwei Fang, Rui Wang, Tao Huang, Liping Jing
In this paper, we propose Multiple Asymptotically Normal Distribution Attacks (MultiANDA), a novel method that explicitly characterizes adversarial perturbations from a learned distribution.
1 code implementation • 12 Jul 2022 • Tao Huang, Lang Huang, Shan You, Fei Wang, Chen Qian, Chang Xu
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias.
1 code implementation • 21 Jun 2022 • Jianan Liu, Liping Bai, Yuxuan Xia, Tao Huang, Bing Zhu, Qing-Long Han
The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry.
no code implementations • 30 May 2022 • Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen
Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.
1 code implementation • 29 May 2022 • Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, Chang Xu
To obtain a group of masks, the receptive tokens are learned via the regular task loss but with teacher fixed, and we also leverage a Dice loss to enrich the diversity of learned masks.
1 code implementation • 21 May 2022 • Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu
In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly.
Ranked #2 on
Knowledge Distillation
on ImageNet
(using extra training data)
no code implementations • 13 May 2022 • Jianan Liu, Hao Li, Tao Huang, Euijoon Ahn, Kang Han, Adeel Razi, Wei Xiang, Jinman Kim, David Dagan Feng
However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images.
2 code implementations • CVPR 2022 • Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks.
no code implementations • 13 Mar 2022 • Weiyi Xiong, Jianan Liu, Yuxuan Xia, Tao Huang, Bing Zhu, Wei Xiang
Deep learning-based instance segmentation enables real-time object identification from the radar detection points.
1 code implementation • ICLR 2022 • Tao Huang, Zekang Li, Hua Lu, Yong Shan, Shusheng Yang, Yang Feng, Fei Wang, Shan You, Chang Xu
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.
no code implementations • 18 Jan 2022 • Tao Huang, Jiachen Wang, Xiao Chen
Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL).
no code implementations • CVPR 2022 • Tao Huang, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu
In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently.
no code implementations • 5 Oct 2021 • Jianan Liu, Weiyi Xiong, Liping Bai, Yuxuan Xia, Tao Huang, Wanli Ouyang, Bing Zhu
Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points.
no code implementations • 29 Sep 2021 • Tao Huang, Xiao Chen, Jiachen Wang
Learning informative representations from image-based observations is a fundamental problem in deep Reinforcement Learning (RL).
no code implementations • 3 Jun 2021 • Hanyuan Hang, Tao Huang, Yuchao Cai, Hanfang Yang, Zhouchen Lin
In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histogram partition and ensemble learning.
1 code implementation • CVPR 2021 • Xiu Su, Tao Huang, Yanxi Li, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once.
1 code implementation • CVPR 2021 • Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi
Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).
no code implementations • ICLR 2021 • Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu
In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly.
11 code implementations • CVPR 2021 • Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, Jianzhuang Liu
In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.
no code implementations • 1 Jan 2021 • Tao Huang, Shan You, Yibo Yang, Zhuozhuo Tu, Fei Wang, Chen Qian, ChangShui Zhang
Differentiable neural architecture search (NAS) has gained much success in discovering more flexible and diverse cell types.
no code implementations • 1 Jan 2021 • Zhuozhuo Tu, Shan You, Tao Huang, DaCheng Tao
Wasserstein distributionally robust optimization (DRO) has recently received significant attention in machine learning due to its connection to generalization, robustness and regularization.
no code implementations • 18 Nov 2020 • Tao Huang, Shan You, Yibo Yang, Zhuozhuo Tu, Fei Wang, Chen Qian, ChangShui Zhang
However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells.
no code implementations • 17 Nov 2020 • Tao Huang, Yihan Zhang, Jiajing Wu, Junyuan Fang, Zibin Zheng
To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers.
no code implementations • 28 Oct 2020 • Xiu Su, Shan You, Tao Huang, Hongyan Xu, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e. g., FLOPs).
1 code implementation • 20 Oct 2020 • Yuxuan Du, Tao Huang, Shan You, Min-Hsiu Hsieh, DaCheng Tao
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices.
no code implementations • CVPR 2020 • Shan You, Tao Huang, Mingmin Yang, Fei Wang, Chen Qian, Chang-Shui Zhang
The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones.
Ranked #72 on
Neural Architecture Search
on ImageNet
no code implementations • ICLR 2020 • Tao Huang, Zhen Han, Xu Jia, Hanyuan Hang
In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.
no code implementations • 20 Dec 2019 • Shujie Han, Jun Wu, Erci Xu, Cheng He, Patrick P. C. Lee, Yi Qiang, Qixing Zheng, Tao Huang, Zixi Huang, Rui Li
To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e. g., Backblaze SMART logs).