Results 41 to 50 of about 322,902 (281)

An Automatic Sample Augmentation Method for Paddy Rice Mapping Based on Segment Anything Model and Phenological Features—A Case Study in Southwest China

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The remote sensing mapping of paddy rice in Southwest China faces challenges such as fragmented parcels and difficulties in field sample collection, hindering deep learning technology applications. To address sample scarcity for deep learning-based paddy
Ziyi Tang, Xinyi Luo, Zijia Yan, Shiyi Li, Sujie Xiao, Hao Li   +5 more
doaj   +1 more source

MCMC for Imbalanced Categorical Data

, 2017
Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty.
Dunson, David B., Johndrow, James E., Pillai, Natesh, Smith, Aaron   +3 more
core   +2 more sources

Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics

IEEE Journal of Biomedical and Health Informatics, 2022
Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition, contrastive learning has recently been shown to hold great promise in learning effective ...
Ruilin Li, Lipo Wang, P. N. Suganthan, Olga Sourina   +3 more
openaire   +4 more sources

Extended Subspace Projection Upon Sample Augmentation Based on Global Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a ...
Jiaochan Hu, Xueji Shen, Haoyang Yu, Xiaodi Shang, Qiandong Guo, Bing Zhang   +5 more
doaj   +1 more source

Sample Efficiency of Data Augmentation Consistency Regularization

, 2022
Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data. In this paper, we take a step in this direction - we first present a simple and novel analysis for linear regression with label invariant augmentations ...
Yang, Shuo, Dong, Yijun, Ward, Rachel, Dhillon, Inderjit S., Sanghavi, Sujay, Lei, Qi   +5 more
openaire   +2 more sources

Dynamic Adaptive Iterative Generative Adversarial Network for Hyperspectral Image Classification With Class Imbalance

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Small-sample learning improves the problem of limited labeled samples in hyperspectral image (HSI) classification to a greater extent, but still suffers from the severe problem of class imbalance, where minority classes are poorly learned and classified,
Ke Li, Ying Cui, Liguo Wang, Shan Gao, Chunhui Zhao, Tianfang Luo   +5 more
doaj   +1 more source

Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks

, 2017
We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a
Dong, Yue, Li, Xiao, Peers, Pieter, Tong, Xin   +3 more
core   +1 more source

DS-SIAUG: A Self-Training Approach Using a Disrupted Student Model for Enhanced Side-Scan Sonar Image Augmentation

Sensors
Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition.
Chengyang Peng, Shaohua Jin, Gang Bian, Yang Cui   +3 more
doaj   +1 more source

Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

Remote Sensing, 2021
For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance.
Wenning Wang, Xuebin Liu, Xuanqin Mou
doaj   +1 more source

Inhibiting stearoyl‐CoA desaturase suppresses bone metastatic prostate cancer by modulating cellular stress, mTOR signaling, and DNA damage response

FEBS Letters, EarlyView.
Bone metastasis in prostate cancer (PCa) patients is a clinical hurdle due to the poor understanding of the supportive bone microenvironment. Here, we identify stearoyl‐CoA desaturase (SCD) as a tumor‐promoting enzyme and potential therapeutic target in bone metastatic PCa.
Alexis Wilson, Mackenzie K Herroon, Shane Mecca, Laimar C Garmo, Jacob Lindquist, Shrila Rajendran, Steve M. Patrick, Izabela Podgorski   +7 more
wiley   +1 more source