Results 41 to 50 of about 29,104 (263)

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, Yukun Liu, Daming Shi, Xiaochun Cheng   +3 more
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

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

Few-shot Learning with Noisy Labels

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Accepted to CVPR ...
Kevin J. Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, Tal Hassner   +3 more
openaire   +2 more sources

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, Xiaolan Guo, Yang Li, Francesco Marinello, Sezai Ercisli, Zhuo Zhang   +5 more
doaj   +1 more source

CEACAM1 participation in breast cancer progression

Molecular Oncology, EarlyView.
In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin, Irina Kube‐Golovin, Anatolii Romaniuk, Gunther Wennemuth   +3 more
wiley   +1 more source

Laplacian Regularized Few-Shot Learning

CoRR, 2020
ICML 2020 ...
Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed   +3 more
openaire   +3 more sources

Dual Prototype Learning for Few Shot Semantic Segmentation

IEEE Access
Few-shot segmentation (FSS) is a challenging task because the same class of targets in the support and query images may have different scales, textures and background information.
Wenxuan Li, Shaobo Chen, Chengyi Xiong
doaj   +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, Nhat Anh Le, Nelson Hebert Minaya, Sang-Il Choi   +3 more
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

A few-shot semantic segmentation method based on feature enhancement of target category

ROBOMECH Journal
Deep learning-based image semantic segmentation techniques have made great strides in recent years. However, they still need large amounts of finely annotated image data, and generalizing the model from known classes to unknown ones remains a challenge ...
Kai Wang, Takayuki Nakamura
doaj   +1 more source