Results 51 to 60 of about 8,885,948 (291)
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
Few Shot Learning for Information Verification
CoRR, 2021 Information verification is quite a challenging task, this is because many times verifying a claim can require picking pieces of information from multiple pieces of evidence which can have a hierarchy of complex semantic relations. Previously a lot of researchers have mainly focused on simply concatenating multiple evidence sentences to accept or ...
Usama Khalid, Mirza Omer Beg
openaire +3 more sources
Few-Shot Unsupervised Domain Adaptation via Meta Learning
, 2022 Unsupervised domain adaptation (UDA) has raised a lot of interests in recent years. However, current UDA methods are still not capable enough in dealing with two issues: 1) the scarcity of labeled data in source domain and 2) the need of a general model ...
Chengmei Yang (20259105) +4 more
core +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
Laplacian Regularized Few-Shot Learning
CoRR, 2020 ICML 2020 ...
Imtiaz Masud Ziko +3 more
openaire +4 more sources
Molecular Oncology, EarlyView.Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur +11 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
Contrastive Graph Few-Shot Learning
CoRR, 2022 Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradation in face of limited annotated data, they excessively rely on labeled data, where the distribution shift in the test phase might result in impaired generalization ability ...
Chunhui Zhang +4 more
openaire +3 more sources
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source