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Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

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Integrating Informativeness, Representativeness and Diversit...

International Joint Conference on Neural Networks (IJCNN)

作者： Ziang Liu Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria - informativeness, representativeness, and diversity - have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.

关键词： Computational modeling Clustering algorithms Machine learning Training Labeling Predictive models image processing

Graph-based scale-aware network for human parsing 1

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Yang, Beibei Yu, Changqian Liu, Jiahui Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

Recent work has made considerable progress in exploring contextual information for human parsing with the Fully Convolutional Network framework. However, there still exist two challenges: (1) inherent relative relationships between parts;(2) scale variation of human parts. To tackle both problems, we propose a Graph-Based Scale-Aware Network for human parsing. First, we embed a Graph-Based Part Reasoning Layer into the backbone network to reason the relative relationship between human parts. Then we construct a Scale-Aware Context Embedding Layer, which consists of two branches to capture scale-specific contextual information, with different receptive fields and scale-specific supervisions. In addition, we adopt an edge supervision to further improve the performance. Extensive experimental evaluations demonstrate that the proposed model performs favorably against the state-of-the-art human parsing methods. More specifically, our algorithm achieves 53.32% (mIoU) on the LIP dataset. © Springer Nature Switzerland AG 2019.

关键词： Embeddings

Data alignment based adversarial defense benchmark for EEG-based BCIs

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Neural Networks 2025年 188卷 107516页

作者： Chen, Xiaoqing Jia, Tianwang Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China Zhongguancun Academy Beijing100080 China

Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based BCIs. This study evaluates nine adversarial defense approaches (including five defense strategies) across five EEG datasets (covering three paradigms), three neural networks, and four experimental scenarios. Our results show for the first time that integrating data augmentation, data alignment, and robust training can further improve both the accuracy and robustness of BCIs compared to using only one or two of them. Furthermore, we provide insights into the characteristics of various adversarial defense approaches based on EEG data alignment, offering valuable guidance for developing more accurate and secure EEG-based BCIs. © 2025 Elsevier Ltd

关键词： Adversarial attack Adversarial defense Brain–computer interface Data alignment Electroencephalogram Security

One-shot distributed algorithm for PCA with RBF kernels

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arXiv 2020年

作者： He, Fan Lv, Kexin Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed scenario. This interesting relationship makes it possible to establish distributed kernel PCA for feature-distributed cases from ideas in distributed PCA in sample-distributed scenario. In theoretical part, we analyze the approximation error for both linear and RBF kernels. The result suggests that when eigenvalues decay fast, the proposed algorithm gives high quality results with low communication cost. This result is also verified by numerical experiments, showing the effectiveness of our algorithm in practice. Copyright © 2020, The Authors. All rights reserved.

关键词： Principal component analysis

A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

MBGD-RDA Training and Rule Pruning for Concise TSK Fuzzy Regression Models

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arXiv 2020年

作者： Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

To effectively train Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a Mini-Batch Gradient Descent with Regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. It has demonstrated superior performances;however, there are also some limitations, e.g., it does not allow the user to specify the number of rules directly, and only Gaussian MFs can be used. This paper proposes two variants of MBGD-RDA to remedy these limitations, and show that they outperform the original MBGD-RDA and the classical ANFIS algorithms with the same number of rules. Furthermore, we also propose a rule pruning algorithm for TSK fuzzy systems, which can reduce the number of rules without significantly sacrificing the regression performance. Experiments showed that the rules obtained from pruning are generally better than training them from scratch directly, especially when Gaussian MFs are used. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

Multi-Level temporal pyramid network for action detection

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arXiv 2020年

作者： Wang, Xiang Gao, Changxin Zhang, Shiwei Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China DAMO Academy Alibaba Group

Currently, one-stage frameworks have been widely applied for temporal action detection, but they still suffer from the challenge that the action instances span a wide range of time. The reason is that these one-stage detectors, e.g., Single Shot Multi-Box Detector (SSD), extract temporal features only applying a single-level layer for each head, which is not discriminative enough to perform classification and regression. In this paper, we propose a Multi-Level Temporal Pyramid Network (MLTPN) to improve the discrimination of the features. Specially, we first fuse the features from multiple layers with different temporal resolutions, to encode multi-layer temporal information. We then apply a multi-level feature pyramid architecture on the features to enhance their discriminative abilities. Finally, we design a simple yet effective feature fusion module to fuse the multi-level multi-scale features. By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations. We evaluate MLTPN on two challenging datasets: THUMOS14 and Activitynet v1.3, and the experimental results show that MLTPN obtains competitive performance on Activitynet v1.3 and outperforms the state-of-the-art approaches on THUMOS14 significantly. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

Combining Attractor Propagation with Differential Evolution for Three-echelon logistics distribution optimization

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SSRN

SSRN 2022年

作者： Zhang, Haifei Ge, Hongwei Yang, Jieming Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Key Laboratory of Advanced Process Control for Light Industry Jiangnan University Ministry of Education Wuxi214122 China School of Computer Science and Engineering Anhui University of Science & Technology Anhui Huainan232001 China Medical Image Processing Group Department of Radiology University of Pennsylvania PhiladelphiaPA19104 United States

In the process of urban logistics distribution, how to achieve efficiently and timely logistics distribution within the distribution time limit has become an urgent problem to be solved in logistics management. Three-echelon logistics distribution is considered to be an effective strategy to alleviate urban congestion, improve liquidity and reduce pollution. In this paper, a mathematical model of three-echelon logistics distribution problem considering time window constraint is established based on directed graph, and a double-tier intelligent algorithm coordinated by Distance Entropy-based Attractor Propagation (DEBAP) clustering algorithm - Crossover-and-Selection based Differential Evolution (CSBDE) is proposed. DEBAP is responsible for the optimization of region partitioning and facility allocation. The obtained region partitioning and facility allocation scheme are transmitted to CSBDE for path planning. CSBDE feeds back the optimized path planning scheme to DEBAP, and the two-tier intelligent algorithm cooperates to complete the optimization of three-echelon logistics distribution system. In addition, this paper proposes a search strategy based on reachable distribution region and a facility allocation optimization strategy based on path length as weight to improve the efficiency of the algorithm. The example calculation shows that the mathematical model and combined intelligent algorithm proposed in this paper can effectively solve 3-echelon logistics distribution problem(3E-LDP), provide theoretical and methodological guidance for multi-echelon logistics distribution, and can be well implemented in practice to help logistics operators reduce operation costs and improve customer service. © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

Single upper limb pose estimation method based on improved stacked hourglass network

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arXiv 2020年

作者： Peng, Gang Zheng, Yuezhi Li, Jianfeng Yang, Jin Deng, Zhonghua Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human–machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model was designed. Deconvolution was employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression was used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments showed that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks