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Estimating Noisy Class Posterior with Part-level labels for Noisy label Learning

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arXiv 2024年

作者： Zhao, Rui Shi, Bin Ruan, Jianfei Pan, Tianze Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Province Key Lab of Big Data Knowledge Engineering Xi’an Jiaotong University China School of Physics Xi’an Jiaotong University China School of Distance Education Xi’an Jiaotong University China

In noisy label learning, estimating noisy class posteriors plays a fundamental role for developing consistent classifiers, as it forms the basis for estimating clean class posteriors and the transition matrix. Existing methods typically learn noisy class posteriors by training a classification model with noisy labels. However, when labels are incorrect, these models may be misled to overemphasize the feature parts that do not reflect the instance characteristics, resulting in significant errors in estimating noisy class posteriors. To address this issue, this paper proposes to augment the supervised information with part-level labels, encouraging the model to focus on and integrate richer information from various parts. Specifically, our method first partitions features into distinct parts by cropping instances, yielding part-level labels associated with these various parts. Subsequently, we introduce a novel single-to-multiple transition matrix to model the relationship between the noisy and part-level labels, which incorporates part-level labels into a classifier-consistent framework. Utilizing this framework with part-level labels, we can learn the noisy class posteriors more precisely by guiding the model to integrate information from various parts, ultimately improving the classification performance. Our method is theoretically sound, while experiments show that it is empirically effective in synthetic and real-world noisy benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Design of 2D/2D β-Ni(OH)₂/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms

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Chemosphere 2024年 352卷 141328页

作者： Ajami Yazdi, Aghil Ebrahimian Pirbazari, Azadeh Esmaeili Khalil Saraei, Fatemeh Esmaeili, Amin Ebrahimian Pirbazari, Ali Akbari Kohnehsari, Atena Derakhshesh, Ali Hybrid Nanomaterials & Environment Lab Fouman Faculty of Engineering College of Engineering University of Tehran Fouman 43581- 39115 Iran Data Mining Research Group Fouman Faculty of Engineering College of Engineering University of Tehran Fouman 43581-39115 Iran Department of Chemical Engineering College of Engineering Technology University of Doha for Science and Technology Arab League St Doha 24449 Qatar Environment Lab Eshtehard Industrial Park Alborz Eshtehard 31881336 Iran

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)₂ nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)₂. The FESEM and TEM micrographs showed that the β-Ni(OH)₂ sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)₂ nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R², SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degr

关键词： 2D/2d p-n heterojunctions data validation Nickel photodeposition Sonophotocatalytic/photocatalytic degradation Supervised machine learning algorithms ZnO nanosheets

Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation

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arXiv 2022年

作者： Wu, Zhonghua Wu, Yicheng Lin, Guosheng Cai, Jianfei Qian, Chen S-Lab. Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Department of Data Science and AI Monash University Australia SenseTime Research

Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in this paper, we advocate applying the consistency constraint under various perturbations to effectively regularize unlabeled 3D points. Specifically, we propose a novel DAT (Dual Adaptive Transformations) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate that our model can effectively leverage the unlabeled 3D points and achieve significant performance gains on both datasets, setting new state-of-the-art performance for weakly supervised point cloud segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

Corrigendum to "ABUS-Net: Graph convolutional network with multi-scale features for breast cancer diagnosis using automated breast ultrasound" [Expert Syst. Appl. (2025) 273 126978] (Expert Systems With Applications (2025) 273, (S0957417425006001), (10.1016/***.2025.126978))

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Expert Systems with Applications 2025年 280卷

作者： Wang, Changyan Guo, Yuqing Chen, Haobo Guo, Qihui He, Haihao Chen, Lin Zhang, Qi Lab School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Institute of Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Department of Ultrasound Huadong Hospital Fudan University Shanghai China

The authors regret that the Acknowledgments information was incomplete. The correct statement of the Acknowledgments section should read: "This work was supported by the National Natural science Foundation of China [62071285], science and Technology Commission of Shanghai Municipality [22Y11911600], the Eastern Scholars Program from Shanghai Municipal Education Commission, and the Shanghai Technical Service Center of science and engineering Computing, Shanghai University." The authors would like to apologise for any inconvenience caused. © 2025 Elsevier Ltd

关键词：

A RESULT BASED PORTABLE FRAMEWORK FOR SPOKEN LANGUAGE UNDERSTANDING

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A RESULT BASED PORTABLE FRAMEWORK FOR SPOKEN LANGUAGE UNDERS...

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Cheng, Lizhi Yang, Wenmian Jia, Weijia Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Guangdong Zhuhai China

ISBN: (纸本)9781665438643

Spoken language understanding (SLU), which is a core component of the task-oriented dialogue system, has made substantial progress in the research of single-turn dialogue. However, the performance in multi-turn dialogue is still not satisfactory in the sense that the existing multi-turn SLU methods have low portability and compatibility for other single-turn SLU models. Further, existing multi-turn SLU methods do not exploit the historical predicted results when predicting the current utterance, which wastes helpful information. To gap those shortcomings, in this paper, we propose a novel Result-based Portable Framework for SLU (RPFSLU). RPFSLU allows most existing single-turn SLU models to obtain the contextual information from multi-turn dialogues and takes full advantage of predicted results in the dialogue history during the current prediction. Experimental results on the public dataset KVRET have shown that all SLU models in baselines acquire enhancement by RPFSLU on multi-turn SLU tasks. © 2021 IEEE

关键词： Speech processing

Expression of Concern for: Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

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Expression of Concern for: Medical Image Fusion Solid Works ...

Sustainable Computing and data Communication Systems (ICSCDS), International Conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9781665491990

ISBN: (纸本)9781665492003

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

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SMOG: Accelerating Subgraph Matching on GPUs

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SMOG: Accelerating Subgraph Matching on GPUs

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

关键词：

Kgrefiner: Knowledge graph refinement for improving accuracy of translational link prediction methods

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arXiv 2021年

作者： Saeedizade, Mohammad Javad Torabian, Najmeh Minaei-Bidgoli, Behrouz Iran University of Science and Technology School of Computer Engineering Data Mining Lab Iran

The Link Prediction is the task of predicting missing relations between entities of the knowledge graph. Recent work in link prediction has attempted to provide a model for increasing link prediction accuracy by using more layers in neural network architecture. In this paper, we propose a novel method of refining the knowledge graph so that link prediction operation can be performed more accurately using relatively fast translational models. Translational link prediction models, such as TransE, TransH, TransD, have less complexity than deep learning approaches. Our method uses the hierarchy of relationships and entities in the knowledge graph to add the entity information as auxiliary nodes to the graph and connect them to the nodes which contain this information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in H@10, MR, MRR. © 2021, CC BY.

关键词： Forecasting

WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining

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arXiv 2025年

作者： Wang, Haoran Huai, Lian Li, Wenbin Qi, Lei Jiang, Xingqun Shi, Yinghuan National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing China National Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing Drum Tower Hospital Jiangsu Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China Lab School of Computer Science and Engineering Southeast University Jiangsu Nanjing China BOE Technology Group Co. Ltd. Beijing China

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in medical images, where most of them focus on training an adaptor for fine-tuning a large amount of pixel-wise annotated medical images following a fully supervised manner. In this paper, to reduce the labeling cost, we investigate a novel weakly-supervised SAM-based segmentation model, namely WeakMedSAM. Specifically, our proposed WeakMedSAM contains two modules: 1) to mitigate severe co-occurrence in medical images, a sub-class exploration module is introduced to learn accurate feature representations. 2) to improve the quality of the class activation maps, our prompt affinity mining module utilizes the prompt capability of SAM to obtain an affinity map for random-walk refinement. Our method can be applied to any SAM-like backbone, and we conduct experiments with SAMUS and EfficientSAM. The experimental results on three popularly-used benchmark datasets, i.e., BraTS 2019, AbdomenCT-1K, and MSD Cardiac dataset, show the promising results of our proposed WeakMedSAM. Our code is available at https://***/wanghr64/WeakMedSAM. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging