Results 1 to 10 of about 10,284,296 (292)

A New Instrument Monitoring Method Based on Few-Shot Learning

Applied Sciences, 2023
As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However,
Beini Zhang, Liping Li, Yetao Lyu, Shuguang Chen, Lin Xu, Guanhua Chen   +5 more
doaj   +1 more source

Looking Back to Lower-Level Information in Few-Shot Learning

Information, 2020
Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples ...
Zhongjie Yu, Sebastian Raschka
doaj   +1 more source

Enhancing Few-Shot Image Classification With Cosine Transformer

IEEE Access, 2023
This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only.
Quang-Huy Nguyen, Cuong Q. Nguyen, Dung D. Le, Hieu H. Pham   +3 more
doaj   +1 more source

CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation

Entropy, 2023
Recent research has shown that visual–text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers.
Shi-Cheng Guo, Shang-Kun Liu, Jing-Yu Wang, Wei-Min Zheng, Cheng-Yu Jiang   +4 more
doaj   +1 more source

Few shot learning for Korean winter temperature forecasts

npj Climate and Atmospheric Science
To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation
Seol-Hee Oh, Yoo-Geun Ham
doaj   +1 more source

Dynamic Knowledge Path Learning for Few-Shot Learning

Big Data Mining and Analytics
Few-shot learning is a challenging task that aims to train a classifier with very limited training samples. Most existing few-shot learning methods mainly focus on mining knowledge from limited training samples as much as possible and ignore the learning
Jingzhu Li, Zhe Yin, Xu Yang, Jianbin Jiao, Ye Ding   +4 more
doaj   +1 more source

Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition

Frontiers in Plant Science, 2022
Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods.
Hong Lin, Rita Tse, Rita Tse, Su-Kit Tang, Su-Kit Tang, Zhen-ping Qiang, Giovanni Pau, Giovanni Pau, Giovanni Pau   +8 more
doaj   +1 more source

Constellation loss: Improving the efficiency of deep metric learning loss functions for the optimal embedding of histopathological images

Journal of Pathology Informatics, 2020
Background: Deep learning diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, and they still require a huge amount of well-annotated data for training, which is often non affordable.
Alfonso Medela, Artzai Picon
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A review on NLP zero-shot and few-shot learning: methods and applications

Discover Applied Sciences
Zero-shot and few-shot learning techniques in natural language processing (NLP), this comprehensive review traces their evolution from traditional methods to cutting-edge approaches like transfer learning and pre-trained language models, semantic ...
G. Ramesh, Mahammad Sahil, Shashank A. Palan, Darshan Bhandary, Teli Abhishek Ashok, J. Shreyas, N. Sowjanya   +6 more
doaj   +1 more source

Malware Classification Using Few-Shot Learning Approach

Information
Malware detection, targeting the microarchitecture of processors, has recently come to light as a potentially effective way to improve computer system security.
Khalid Alfarsi, Saim Rasheed, Iftikhar Ahmad   +2 more
doaj   +1 more source