Results 41 to 50 of about 517,396 (274)
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
doaj +1 more source
Zero-Shot Cross-Lingual Transfer with Meta Learning
, 2020 Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance.
Augenstein, Isabelle +3 more
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Interventional Few-Shot Learning
, 2020 We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels.
YUE, Zhongqi +3 more
openaire +2 more sources
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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Low-shot Visual Recognition by Shrinking and Hallucinating Features
, 2017 Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way.
Girshick, Ross, Hariharan, Bharath
core +1 more source
, 2018 We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers.
Han, Xu +6 more
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Generative knowledge-based transfer learning for few-shot health condition estimation
Complex & Intelligent Systems, 2022 In the field of high-end manufacturing, it is valuable to study few-shot health condition estimation. Although transfer learning and other methods have effectively improved the ability of few-shot learning, they still cannot solve the lack of prior ...
Weijie Kang, Jiyang Xiao, Junjie Xue
doaj +1 more source
A Contrastive Model with Local Factor Clustering for Semi-Supervised Few-Shot Learning
Mathematics, 2023 Learning novel classes with a few samples per class is a very challenging task in deep learning. To mitigate this issue, previous studies have utilized an additional dataset with extensively labeled samples to realize transfer learning.
Hexiu Lin +3 more
doaj +1 more source
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
, 2019 The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision.
Huang, Huaxi +4 more
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Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source