Results 61 to 70 of about 8,885,948 (291)
Dual Prototype Learning for Few Shot Semantic Segmentation
IEEE AccessFew-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
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
Few-Shot Class-Incremental Learning [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the ...
Xiaoyu Tao +5 more
openaire +4 more sources
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 +3 more
wiley +1 more source
A few-shot semantic segmentation method based on feature enhancement of target category
ROBOMECH JournalDeep 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
Meta learning for few shot learning [PDF]
, 2022 Few-shot learning aims to scale visual recognition to open-ended growth of new classes with limited labelled examples, thus alleviating data and computation bottleneck of conventional deep learning. This thesis proposes a meta learning (a.k.a.
Zhang, Xueting
core +1 more source
Few-shot Learning for Spatial Regression
CoRR, 2020 We propose a few-shot learning method for spatial regression. Although Gaussian processes (GPs) have been successfully used for spatial regression, they require many observations in the target task to achieve a high predictive performance. Our model is trained using spatial datasets on various attributes in various regions, and predicts values on ...
Tomoharu Iwata, Yusuke Tanaka 0002
openaire +3 more sources
Few-Shot Learning for Biometric Verification
CoRR, 2022 In machine learning applications, it is common practice to feed as much information as possible. In most cases, the model can handle large data sets that allow to predict more accurately. In the presence of data scarcity, a Few-Shot learning (FSL) approach aims to build more accurate algorithms with limited training data.
Umaid M. Zaffar +3 more
openaire +3 more sources
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 +2 more
wiley +1 more source
Plain Template Insertion: Korean-Prompt-Based Engineering for Few-Shot Learners
IEEE Access, 2022 Prompt-based learning is a method used for language models to interpret natural language by remembering the prior knowledge acquired and the training objective.
Jaehyung Seo +7 more
doaj +1 more source