Results 1 to 10 of about 514,020 (257)
Tuberculosis detection using few shot learning [PDF]
Scientific ReportsTuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good results
Kamran Riasat +5 more
doaj +4 more sources
Federated knee injury diagnosis using few shot learning [PDF]
Frontiers in Artificial IntelligenceIntroductionKnee injuries, especially Anterior Cruciate Ligament (ACL) tears and meniscus tears, are becoming increasingly common and can severely restrict mobility and quality of life.
Chirag Goel, Anita X, Jani Anbarasi L
doaj +2 more sources
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 +3 more sources
A novel framework GRCornShot for corn disease detection using few shot learning with prototypical network [PDF]
Scientific ReportsPrecision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods.
Ruchi Rani +3 more
doaj +2 more sources
Survey on Machine Learning Methods for Small Sample Data [PDF]
Jisuanji gongcheng, 2022 Few-shot learning is a type of machine learning method for small sample data that operates by using less supervised sample data to build machine learning models that can solve practical problems.Therefore, few-shot learning can be used to solve the ...
CHEN Liangchen, FU Deyin
doaj +1 more source
Multi-Similarity Enhancement Network for Few-Shot Segmentation
IEEE Access, 2023 Few-Shot Segmentation (FSS) is challenging for intra-class diversity and support sample scarcity. Many works focus on the class-wise or pixel-wise similarity between the support foreground and query sample while neglecting the support background, which ...
Hao Chen, Zhe-Ming Lu, Yang-Ming Zheng
doaj +1 more source
Filtering Specialized Change in a Few-Shot Setting
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The aim of change detection in remote sensing usually is not to find all differences between the observations, but rather only specific types of change, such as urban development, deforestation, or even more specialized categories like roadwork. However,
Martin Hermann +2 more
doaj +1 more source
HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification
International Journal of Applied Earth Observations and Geoinformation, 2023 Few-shot learning is an important and challenging research topic for remote sensing image scene classification. Many existing approaches address this challenge by using meta-learning and metric-learning techniques, which aim to develop feature extractors
Junjie Zhu +4 more
doaj +1 more source
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand.
Jinxiang Liu, Laura Brown
doaj +1 more source
Semantic Matching Network for Few-Shot Learning [PDF]
Jisuanji gongcheng, 2021 In the field of deep learning,it is difficult to achieve visual recognition with a small number of samples.To address the problem,this paper proposes a semantic matching network.The dual attention mechanism is used to match the semantic information of ...
WANG Ronggui, TANG Mingkong, YANG Juan, XUE Lixia, HU Min
doaj +1 more source