55   Artículos

 
en línea
Chuanyun Xu, Hang Wang, Yang Zhang, Zheng Zhou and Gang Li    
Few-shot learning refers to training a model with a few labeled data to effectively recognize unseen categories. Recently, numerous approaches have been suggested to improve the extraction of abundant feature information at hierarchical layers or multipl... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Jie Ren, Changmiao Li, Yaohui An, Weichuan Zhang and Changming Sun    
Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature representa... ver más
Revista: AI    Formato: Electrónico

 
en línea
Xiaodong Cui, Zhuofan He, Yangtao Xue, Keke Tang, Peican Zhu and Jing Han    
Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot le... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Andrei Paraschiv, Teodora Andreea Ion and Mihai Dascalu    
The advent of online platforms and services has revolutionized communication, enabling users to share opinions and ideas seamlessly. However, this convenience has also brought about a surge in offensive and harmful language across various communication m... ver más
Revista: Information    Formato: Electrónico

 
en línea
Yingying Liang, Peng Zhao and Yimeng Wang    
Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a chall... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Shiwei Ruan, Hao Cang, Huixin Chen, Tianying Yan, Fei Tan, Yuan Zhang, Long Duan, Peng Xing, Li Guo, Pan Gao and Wei Xu    
Early detection and diagnosis of crop anomalies is crucial for enhancing crop yield and quality. Recently, the combination of machine learning and deep learning with hyperspectral images has significantly improved the efficiency of crop detection. Howeve... ver más
Revista: Agronomy    Formato: Electrónico

 
en línea
Dan Liu, Ting Liu, Hai Bi, Yunpeng Zhao and Yuan Cheng    
In the marine ecological environment, marine microalgae is an important photosynthetic autotrophic organism, which can carry out photosynthesis and absorb carbon dioxide. With the increasingly serious eutrophication of the water body, under certain envir... ver más
Revista: Water    Formato: Electrónico

 
en línea
Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, Paul A. Watters and Seyit Camtepe    
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware familie... ver más
Revista: Future Internet    Formato: Electrónico

 
en línea
Xuyang Wang, Yajun Du, Danroujing Chen, Xianyong Li, Xiaoliang Chen, Yongquan Fan, Chunzhi Xie, Yanli Li and Jia Liu    
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previ... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Haoyu Chen, Stacy Lindshield, Papa Ibnou Ndiaye, Yaya Hamady Ndiaye, Jill D. Pruetz and Amy R. Reibman    
Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a rea... ver más
Revista: AI    Formato: Electrónico

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