Results 31 to 40 of about 514,020 (257)
Improving Augmentation Efficiency for Few-Shot Learning
IEEE Access, 2022 While human intelligence can easily recognize some characteristics of classes with one or few examples, learning from few examples is a challenging task in machine learning.
Wonhee Cho, Eunwoo Kim
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Graph Few-shot Learning via Knowledge Transfer
, 2020 Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating ...
Chawla, Nitesh V. +7 more
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Active Few-Shot Learning with FASL
, 2022 Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train models with good quality. This makes it challenging to quickly develop and deploy new models for real world problems and business needs.
Thomas Müller +3 more
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Few-Shot Classification with Contrastive Learning
, 2022 To appear in ECCV ...
Zhanyuan Yang +2 more
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VEHICLE DETECTION AND IDENTIFICATION WITH SMALL DATASET USING FEW-SHOT LEARNING
International Journal of Advances in Signal and Image Sciences, 2023 Vehicle detection and identification serve an important role in employing autonomous vehicle classification. However, most deep learning methods for vehicle detection rely on large number of datasets for the training to perform well. The dataset shortage
Muzakki Afandi, Media Anugerah Ayu
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Robust Compare Network for Few-Shot Learning
IEEE Access, 2020 Making machines learn like humans is the ultimate goal of artificial intelligence. Few-shot learning attempts to simulate the learning mechanism of humans, which is a task that can learn novel concepts from very few labeled samples.
Yixin Yang +4 more
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Learning few-shot imitation as cultural transmission
Nature Communications, 2023 Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. It can be thought of as the process that perpetuates fit variants in cultural evolution.
Avishkar Bhoopchand +17 more
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RankDNN: Learning to Rank for Few-Shot Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic.
Guo, Qianyu +6 more
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Adaptive Learning Knowledge Networks for Few-Shot Learning
IEEE Access, 2019 In recent years, relying on training with thousands of labeled samples, deep learning has achieved remarkable success in the field of computer vision. However, in practice, annotating samples is a time-consuming and laborious task, which means that it is
Minghao Yan
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HoloDetect: Few-Shot Learning for Error Detection
, 2019 We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement. Our framework consists of
Bengio Yoshua +9 more
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