Results 41 to 50 of about 514,020 (257)
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
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
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
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
core +1 more source
Active Instance Selection for Few-Shot Classification
IEEE Access, 2022 Few-shot learning aims to develop well-trained models by using only a few annotated samples. However, the performance of few-shot learning deteriorates if inappropriate support samples are selected.
Junsup Shin +3 more
doaj +1 more source
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 +5 more
doaj +1 more source
A Meta-Learning Approach for Custom Model Training
, 2019 Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e.
Abrishami, Mohammad Saeed +3 more
core +1 more source
Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Multimodal Few-Shot Learning for Gait Recognition
Applied Sciences, 2020 A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to ...
Jucheol Moon +3 more
doaj +1 more source
A survey of few-shot learning in smart agriculture: developments, applications, and challenges
Plant Methods, 2022 With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples.
Jiachen Yang +5 more
doaj +1 more source