Search Results for author: Shuo Wang

Found 104 papers, 33 papers with code

Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery

no code implementations ECCV 2020 Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, Meng Wang

It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results.

Few-Shot Learning

Dual Adversarial Network for Deep Active Learning

no code implementations ECCV 2020 Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng

In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.

Active Learning

Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition

no code implementations ECCV 2020 Xiaobo Wang, Tianyu Fu, Shengcai Liao, Shuo Wang, Zhen Lei, Tao Mei

Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.

Face Recognition Knowledge Distillation

EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)

no code implementations23 Dec 2022 Haoran Wang, Yan Zhu, Wenzheng Qin, Yizhe Zhang, Pinghong Zhou, QuanLin Li, Shuo Wang, Zhijian Song

In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis.

Anomaly Detection Density Estimation

Source-free Depth for Object Pop-out

no code implementations10 Dec 2022 Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, Luc van Gool

In this work, we adapt such depth inference models for object segmentation using the objects' ``pop-out'' prior in 3D.

object-detection Object Detection +2

Tracking Dataset IP Use in Deep Neural Networks

no code implementations24 Nov 2022 Seonhye Park, Alsharif Abuadbba, Shuo Wang, Kristen Moore, Yansong Gao, Hyoungshick Kim, Surya Nepal

In this work, we propose a novel DNN fingerprinting technique dubbed DEEPTASTER to prevent a new attack scenario in which a victim's data is stolen to build a suspect model.

Data Augmentation Transfer Learning

DETRDistill: A Universal Knowledge Distillation Framework for DETR-families

no code implementations17 Nov 2022 Jiahao Chang, Shuo Wang, Guangkai Xu, Zehui Chen, Chenhongyi Yang, Feng Zhao

Transformer-based detectors (DETRs) have attracted great attention due to their sparse training paradigm and the removal of post-processing operations, but the huge model can be computationally time-consuming and difficult to be deployed in real-world applications.

Knowledge Distillation object-detection +1

Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI

no code implementations11 Nov 2022 Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Amit R. Patel, Miaomiao Zhang

Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect.

Myocardium Segmentation

Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI

no code implementations11 Nov 2022 Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Frederick H. Epstein, Amit R. Patel, Miaomiao Zhang

With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification.

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

1 code implementation28 Aug 2022 Mengyun Qiao, Berke Doga Basaran, Huaqi Qiu, Shuo Wang, Yi Guo, Yuanyuan Wang, Paul M. Matthews, Daniel Rueckert, Wenjia Bai

Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.

Anatomy

Improved post-hoc probability calibration for out-of-domain MRI segmentation

no code implementations4 Aug 2022 Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz, Daniel Rueckert

In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable.

Image Segmentation MRI segmentation +1

Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP

1 code implementation15 Jul 2022 Zhicai Wang, Yanbin Hao, Xingyu Gao, Hao Zhang, Shuo Wang, Tingting Mu, Xiangnan He

They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.

TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection

1 code implementation4 Jul 2022 Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang

We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training.

object-detection RGB-D Salient Object Detection +1

Trichomonas Vaginalis Segmentation in Microscope Images

no code implementations3 Jul 2022 Lin Li, Jingyi Liu, Shuo Wang, Xunkun Wang, Tian-Zhu Xiang

Trichomoniasis is a common infectious disease with high incidence caused by the parasite Trichomonas vaginalis, increasing the risk of getting HIV in humans if left untreated.

object-detection Object Detection

Boundary-Guided Camouflaged Object Detection

1 code implementation2 Jul 2022 Yujia Sun, Shuo Wang, Chenglizhao Chen, Tian-Zhu Xiang

Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task.

object-detection Object Detection +1

Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior

1 code implementation8 Jun 2022 Chen Qin, Shuo Wang, Chen Chen, Wenjia Bai, Daniel Rueckert

In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model.

Image Registration

Suggestive Annotation of Brain MR Images with Gradient-guided Sampling

no code implementations2 Jun 2022 Chengliang Dai, Shuo Wang, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai

We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation.

Brain Segmentation Image Segmentation +1

A Template-based Method for Constrained Neural Machine Translation

1 code implementation23 May 2022 Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu

In this work, we propose a template-based method that can yield results with high translation quality and match accuracy and the inference speed of our method is comparable with unconstrained NMT models.

Machine Translation NMT +1

Understanding and Mitigating the Uncertainty in Zero-Shot Translation

no code implementations20 May 2022 Wenxuan Wang, Wenxiang Jiao, Shuo Wang, Zhaopeng Tu, Michael R. Lyu

Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation (MNMT) system.

Machine Translation Translation

DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning

no code implementations11 May 2022 Xiaoqin Zhang, Ziwei Huang, Jingjing Zheng, Shuo Wang, Xianta Jiang

The task of grasp pattern recognition aims to derive the applicable grasp types of an object according to the visual information.

InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy

no code implementations5 May 2022 Weichen Fan, Yuanbo Yang, Kunpeng Qiu, Shuo Wang, Yongxin Guo

Therefore, to address the generalization problem in GI(Gastrointestinal) endoscopy, we propose a multi-domain GI dataset and a light, plug-in block called InvNorm(Invertible Normalization), which could achieve a better generalization performance in any structure.

Domain Generalization Ethics +2

Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

1 code implementation23 Apr 2022 Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim

To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.

Graph Attention Spatio-Temporal Forecasting

Attention in Attention: Modeling Context Correlation for Efficient Video Classification

1 code implementation20 Apr 2022 Yanbin Hao, Shuo Wang, Pei Cao, Xinjian Gao, Tong Xu, Jinmeng Wu, Xiangnan He

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts.

Video Classification

Towards Web Phishing Detection Limitations and Mitigation

no code implementations3 Apr 2022 Alsharif Abuadbba, Shuo Wang, Mahathir Almashor, Muhammed Ejaz Ahmed, Raj Gaire, Seyit Camtepe, Surya Nepal

However, with an average of 10K phishing links reported per hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of such ML-based solutions are laid bare.

PublicCheck: Public Integrity Verification for Services of Run-time Deep Models

no code implementations21 Mar 2022 Shuo Wang, Sharif Abuadbba, Sidharth Agarwal, Kristen Moore, Ruoxi Sun, Minhui Xue, Surya Nepal, Seyit Camtepe, Salil Kanhere

Existing integrity verification approaches for deep models are designed for private verification (i. e., assuming the service provider is honest, with white-box access to model parameters).

Model Compression

CAFE: Learning to Condense Dataset by Aligning Features

2 code implementations CVPR 2022 Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, Yang You

Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.

Dataset Condensation

Conservative Distributional Reinforcement Learning with Safety Constraints

no code implementations18 Jan 2022 Hengrui Zhang, Youfang Lin, Sheng Han, Shuo Wang, Kai Lv

Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process.

Distributional Reinforcement Learning reinforcement-learning +1

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

1 code implementation11 Jan 2022 Hanrui Wang, Shuo Wang, Zhe Jin, Yandan Wang, Cunjian Chen, Massimo Tistarell

This technique applies to both white-box and gray-box attacks against authentication systems that determine genuine or imposter users using the dissimilarity score.

Adversarial Attack Face Recognition

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

1 code implementation19 Dec 2021 Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Datwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gomez, Pablo Arbelaez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-min Pei, Murat AK, Sarahi Rosas-Gonzalez, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Lofstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andre Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.

Brain Tumor Segmentation Image Segmentation +2

Agent-Centric Relation Graph for Object Visual Navigation

no code implementations29 Nov 2021 Xiaobo Hu, Zhihao Wu, Kai Lv, Shuo Wang, Youfang Lin

In the navigation task, we introduce an Agent-Centric Relation Graph (ACRG) for learning the visual representation based on the relationships in the environment.

Visual Navigation

MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators

1 code implementation ACL 2022 Zhixing Tan, Xiangwen Zhang, Shuo Wang, Yang Liu

Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks.

Machine Translation Translation

Meta-Imitation Learning by Watching Video Demonstrations

no code implementations ICLR 2022 Jiayi Li, Tao Lu, Xiaoge Cao, Yinghao Cai, Shuo Wang

Our approach relies only on human videos and does not require robot demonstration, which facilitates data collection and is more in line with human imitation behavior.

Imitation Learning

Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

no code implementations8 Jul 2021 Shuo Wang, Chen Qin, Nicolo Savioli, Chen Chen, Declan O'Regan, Stuart Cook, Yike Guo, Daniel Rueckert, Wenjia Bai

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.

Anatomy Cardiac Segmentation +1

Language Models are Good Translators

no code implementations25 Jun 2021 Shuo Wang, Zhaopeng Tu, Zhixing Tan, Wenxuan Wang, Maosong Sun, Yang Liu

Inspired by the recent progress of large-scale pre-trained language models on machine translation in a limited scenario, we firstly demonstrate that a single language model (LM4MT) can achieve comparable performance with strong encoder-decoder NMT models on standard machine translation benchmarks, using the same training data and similar amount of model parameters.

Language Modelling Machine Translation +2

On the Language Coverage Bias for Neural Machine Translation

no code implementations Findings (ACL) 2021 Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu

Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice.

Data Augmentation Machine Translation +2

An Efficient Training Approach for Very Large Scale Face Recognition

1 code implementation CVPR 2022 Kai Wang, Shuo Wang, Panpan Zhang, Zhipeng Zhou, Zheng Zhu, Xiaobo Wang, Xiaojiang Peng, Baigui Sun, Hao Li, Yang You

This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.

 Ranked #1 on Face Verification on IJB-C (training dataset metric)

Face Recognition Face Verification

Robust Training Using Natural Transformation

no code implementations10 May 2021 Shuo Wang, Lingjuan Lyu, Surya Nepal, Carsten Rudolph, Marthie Grobler, Kristen Moore

We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations (NaTra) of the inputs, which are then used to augment the training dataset of the image classifier.

Data Augmentation Image Classification +1

OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning

no code implementations3 May 2021 Shuo Wang, Surya Nepal, Kristen Moore, Marthie Grobler, Carsten Rudolph, Alsharif Abuadbba

We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation.

Disentanglement Federated Learning

High-quality Low-dose CT Reconstruction Using Convolutional Neural Networks with Spatial and Channel Squeeze and Excitation

no code implementations1 Apr 2021 Jingfeng Lu, Shuo Wang, Ping Li, Dong Ye

Low-dose computed tomography (CT) allows the reduction of radiation risk in clinical applications at the expense of image quality, which deteriorates the diagnosis accuracy of radiologists.

Computed Tomography (CT) Image Reconstruction

Product semantics translation from brain activity via adversarial learning

no code implementations29 Mar 2021 Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo

To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.

EEG Translation

IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT

no code implementations28 Mar 2021 Min Wang, Shanchen Pang, Tong Ding, Sibo Qiao, Xue Zhai, Shuo Wang, Neal N. Xiong, Zhengwen Huang

In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).

Few-Shot Learning

A General Framework for Revealing Human Mind with auto-encoding GANs

no code implementations10 Feb 2021 Pan Wang, Rui Zhou, Shuo Wang, Ling Li, Wenjia Bai, Jialu Fan, Chunlin Li, Peter Childs, Yike Guo

For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.

Brain Decoding

GenAD: General Representations of Multivariate Time Series for Anomaly Detection

no code implementations1 Jan 2021 Xiaolei Hua, Su Wang, Lin Zhu, Dong Zhou, Junlan Feng, Yiting Wang, Chao Deng, Shuo Wang, Mingtao Mei

However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.

Management Time Series +1

Neural Machine Translation: A Review of Methods, Resources, and Tools

no code implementations31 Dec 2020 Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu

Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers.

Data Augmentation Machine Translation +2

LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network

1 code implementation27 Dec 2020 Xudong Lv, Boya Wang, Dong Ye, Shuo Wang

In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors.

Translation

ACDER: Augmented Curiosity-Driven Experience Replay

no code implementations16 Nov 2020 Boyao Li, Tao Lu, Jiayi Li, Ning Lu, Yinghao Cai, Shuo Wang

Exploration in environments with sparse feedback remains a challenging research problem in reinforcement learning (RL).

Semantic Flow-guided Motion Removal Method for Robust Mapping

no code implementations14 Oct 2020 Xudong Lv, Boya Wang, Dong Ye, Shuo Wang

In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions.

Instance Segmentation Optical Flow Estimation +1

Detection of Genuine and Posed Facial Expressions of Emotion: A Review

no code implementations26 Aug 2020 Shan Jia, Shuo Wang, Chuanbo Hu, Paula Webster, Xin Li

Facial expressions of emotion play an important role in human social interactions.

A Multimodal Late Fusion Model for E-Commerce Product Classification

no code implementations14 Aug 2020 Ye Bi, Shuo Wang, Zhongrui Fan

The cataloging of product listings is a fundamental problem for most e-commerce platforms.

Classification General Classification

A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter

no code implementations14 Aug 2020 Ye Bi, Shuo Wang, Zhongrui Fan

The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion.

Learning-To-Rank

Loss Function Search for Face Recognition

1 code implementation ICML 2020 Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei

In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.

AutoML Face Recognition

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

no code implementations26 Jun 2020 Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.

BIG-bench Machine Learning Image Segmentation +1

Realistic Adversarial Data Augmentation for MR Image Segmentation

1 code implementation23 Jun 2020 Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert

In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.

Data Augmentation Image Segmentation +2

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 Jun 2020 Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai

Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.

Image Segmentation MRI segmentation +1

Adversarial Defense by Latent Style Transformations

no code implementations17 Jun 2020 Shuo Wang, Surya Nepal, Alsharif Abuadbba, Carsten Rudolph, Marthie Grobler

The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e. g., slightly changing the facial expression of human portraits.

Adversarial Defense

Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

1 code implementation8 Jun 2020 Chen Qin, Shuo Wang, Chen Chen, Huaqi Qiu, Wenjia Bai, Daniel Rueckert

The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible.

Image Registration

On the Inference Calibration of Neural Machine Translation

1 code implementation ACL 2020 Shuo Wang, Zhaopeng Tu, Shuming Shi, Yang Liu

Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output.

Machine Translation NMT +1

An Epidemiological Modelling Approach for Covid19 via Data Assimilation

1 code implementation25 Apr 2020 Philip Nadler, Shuo Wang, Rossella Arcucci, Xian Yang, Yike Guo

We compare and discuss model results which conducts updates as new observations become available.

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

no code implementations22 Mar 2020 Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen

Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.

MULTI-VIEW LEARNING regression +1

Efficient Deep Representation Learning by Adaptive Latent Space Sampling

no code implementations19 Mar 2020 Yuanhan Mo, Shuo Wang, Chengliang Dai, Rui Zhou, Zhongzhao Teng, Wenjia Bai, Yike Guo

Supervised deep learning requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.

General Classification Image Classification +2

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

2 code implementations ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2020 Shuo Wang, Yan-ran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao

When predicting PM2. 5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period.

Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study

1 code implementation8 Feb 2020 Ke Li, Zilin Xiang, Tao Chen, Shuo Wang, Kay Chen Tan

Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.

Transfer Learning

Defending Adversarial Attacks via Semantic Feature Manipulation

no code implementations3 Feb 2020 Shuo Wang, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen

In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.

General Classification

Suggestive Labelling for Medical Image Analysis by Adaptive Latent Space Sampling

no code implementations MIDL 2019 Yuanhan Mo, Shuo Wang, Chengliang Dai, Zhongzhao Teng, Wenjia Bai, Yike Guo

Supervised deep learning for medical imaging analysis requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for medical segmentation tasks), which are expensive and time-consuming to obtain.

Informativeness

OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning

no code implementations18 Jan 2020 Shuo Wang, Tianle Chen, Shangyu Chen, Carsten Rudolph, Surya Nepal, Marthie Grobler

Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.

Anomaly Detection Disentanglement

Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models

no code implementations10 Jan 2020 Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen

In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.

EEG Electrocardiography (ECG) +2

Mis-classified Vector Guided Softmax Loss for Face Recognition

no code implementations26 Nov 2019 Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, Tao Mei

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.

Face Recognition

A Two-stream End-to-End Deep Learning Network for Recognizing Atypical Visual Attention in Autism Spectrum Disorder

no code implementations26 Nov 2019 Jin Xie, Longfei Wang, Paula Webster, Yang Yao, Jiayao Sun, Shuo Wang, Huihui Zhou

In this study, we developed a novel two-stream deep learning network for this recognition based on 700 images and corresponding eye movement patterns of ASD and TD, and obtained an accuracy of 0. 95, which was higher than the previous state-of-the-art.

Classification General Classification

Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data

no code implementations1 Nov 2019 Ziyuan Pu, Zhiyong Cui, Shuo Wang, Qianmu Li, Yinhai Wang

The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.

Friction

Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

no code implementations1 Nov 2019 Ziyuan Pu, Shuo Wang, Chenglong Liu, Zhiyong Cui, Yinhai Wang

A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity.

Decision Making Friction +1

REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

no code implementations29 Sep 2019 Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, Yun Liang

To achieve real-time, highly-efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and develop an efficient processing element (PE) structure supporting the heterogeneous weight quantization, CONV dataflow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource.

Model Compression object-detection +2

Improving Back-Translation with Uncertainty-based Confidence Estimation

1 code implementation IJCNLP 2019 Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, Maosong Sun

While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy.

Low-Resource Neural Machine Translation NMT +1

Defeating Misclassification Attacks Against Transfer Learning

no code implementations29 Aug 2019 Bang Wu, Shuo Wang, Xingliang Yuan, Cong Wang, Carsten Rudolph, Xiangwen Yang

To avoid the bloated ensemble size during inference, we propose a two-phase defence, in which inference from the Student model is firstly performed to narrow down the candidate differentiators to be assembled, and later only a small, fixed number of them can be chosen to validate clean or reject adversarial inputs effectively.

Network Pruning Transfer Learning

Tutorial: Complexity analysis of Singular Value Decomposition and its variants

3 code implementations28 Jun 2019 Xiaocan Li, Shuo Wang, Yinghao Cai

We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity.

A support vector regression-based multi-fidelity surrogate model

no code implementations22 Jun 2019 Maolin Shi, Shuo Wang, Wei Sun, Liye Lv, Xueguan Song

Computational simulations with different fidelity have been widely used in engineering design.

regression

Hindsight Generative Adversarial Imitation Learning

no code implementations19 Mar 2019 Naijun Liu, Tao Lu, Yinghao Cai, Boyao Li, Shuo Wang

Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available.

Imitation Learning reinforcement Learning

Improved Selective Refinement Network for Face Detection

no code implementations20 Jan 2019 Shifeng Zhang, Rui Zhu, Xiaobo Wang, Hailin Shi, Tianyu Fu, Shuo Wang, Tao Mei, Stan Z. Li

With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.

Data Augmentation Face Detection +1

A General Deep Learning Framework for Network Reconstruction and Dynamics Learning

1 code implementation30 Dec 2018 Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin, Jiang Zhang

We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.

Time Series

Support Vector Guided Softmax Loss for Face Recognition

3 code implementations29 Dec 2018 Xiaobo Wang, Shuo Wang, Shifeng Zhang, Tianyu Fu, Hailin Shi, Tao Mei

Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.

Face Recognition

Detect Globally, Refine Locally: A Novel Approach to Saliency Detection

no code implementations CVPR 2018 Tiantian Wang, Lihe Zhang, Shuo Wang, Huchuan Lu, Gang Yang, Xiang Ruan, Ali Borji

Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position.

object-detection RGB Salient Object Detection +2

Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

no code implementations20 Mar 2018 Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Yun Liang

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.

Model Compression Time Series

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

no code implementations14 Mar 2018 Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Yanzhi Wang, Qinru Qiu, Yun Liang

The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.

Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)

no code implementations28 Jul 2017 Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao

It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift.

A Systematic Study of Online Class Imbalance Learning with Concept Drift

no code implementations20 Mar 2017 Shuo Wang, Leandro L. Minku, Xin Yao

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift.

Weakly Supervised Learning for Attribute Localization in Outdoor Scenes

no code implementations CVPR 2013 Shuo Wang, Jungseock Joo, Yizhou Wang, Song-Chun Zhu

We evaluate the proposed method by (i) showing the improvement of attribute recognition accuracy; and (ii) comparing the average precision of localizing attributes to the scene parts.

Association

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