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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
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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
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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
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MetaChest: generalized few-shot learning of pathologies from chest X-rays [PDF]
Visual Computing for Industry, Biomedicine, and ArtThe limited availability of annotated data presents a major challenge in applying deep learning methods to medical image analysis. Few-shot learning methods aim to recognize new classes from only a few labeled examples.
Berenice Montalvo-Lezama +1 more
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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
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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
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem.
Wenbin Li +6 more
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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
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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
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Few-Shot Learning With Geometric Constraints [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 In this article, we consider the problem of few-shot learning for classification. We assume a network trained for base categories with a large number of training examples, and we aim to add novel categories to it that have only a few, e.g., one or five, training examples.
Hong-Gyu Jung, Seong-Whan Lee
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