|
|
|
|
|
Tingkai Hu, Zuqin Chen, Jike Ge, Zhaoxu Yang and Jichao Xu
Insufficiently labeled samples and low-generalization performance have become significant natural language processing problems, drawing significant concern for few-shot text classification (FSTC). Advances in prompt learning have significantly improved t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xuefeng Zhang, Youngsung Kim, Young-Chul Chung, Sangcheol Yoon, Sang-Yong Rhee and Yong Soo Kim
Large-scale datasets, which have sufficient and identical quantities of data in each class, are the main factor in the success of deep-learning-based classification models for vision tasks. A shortage of sufficient data and interclass imbalanced data dis...
ver más
|
|
|
|
|
|
|
|
|
|
|
Junhua Ren and Feng Liu
Power law describes a common behavior in which a few factors play decisive roles in one thing. Most software defects occur in very few instances. In this study, we proposed a novel approach that adopts power law function characteristics for software defe...
ver más
|
|
|
|
|
|
|
|
|
|
|
Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi
Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processin...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dawei Luo, Heng Zhou, Joonsoo Bae and Bom Yun
Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distrib...
ver más
|
|
|
|
|
|
|
|
|
|
|
Alexander Chowdhury, Jacob Rosenthal, Jonathan Waring and Renato Umeton
Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shangchen Ma and Chunlin Song
Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve good...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yugen Yi, Haoming Zhang, Ningyi Zhang, Wei Zhou, Xiaomei Huang, Gengsheng Xie and Caixia Zheng
As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature se...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ioannis E. Livieris, Andreas Kanavos, Vassilis Tampakas and Panagiotis Pintelas
During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled resea...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zitong Yan, Hongmei Liu, Laifa Tao, Jian Ma and Yujie Cheng
To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dezhi Cao, Yue Zhao and Licheng Wu
The construction of pronunciation dictionaries relies on high-quality and extensive training data in data-driven way. However, the manual annotation of corpus for this purpose is both costly and time consuming, especially for low-resource languages that ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Peng Chen and Huibing Wang
Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most exi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yongkun Deng, Chenghao Zhang, Nan Yang and Huaming Chen
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hong Wang, Qingsong Xu and Lifeng Zhou
To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML) form) or not. Previous studies have focused on supervised classification with labeled examples. However, ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhigang Song, Daisong Li, Zhongyou Chen and Wenqin Yang
The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitiga...
ver más
|
|
|
|
|
|
|
|
|
|
|
Julio Jerison E. Macrohon, Charlyn Nayve Villavicencio, X. Alphonse Inbaraj and Jyh-Horng Jeng
With the increasing popularity of Twitter as both a social media platform and a data source for companies, decision makers, advertisers, and even researchers alike, data have been so massive that manual labeling is no longer feasible. This research uses ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaoling Tao, Deyan Kong, Yi Wei and Yong Wang
Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dongming Wang, Li Xu, Wei Gao, Hongwei Xia, Ning Guo and Xiaohan Ren
As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jie Zhang, Fan Li, Xin Zhang, Yue Cheng and Xinhong Hei
As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduc...
ver más
|
|
|
|
|
|