Results 101 to 110 of about 9,759,453 (201)

Small-sample-data augmentation and transfer strategies for forest cover change monitoring

Ecological Indicators
The Qilian Mountains serves as a critical ecological barrier in northwest China, where the forest coverage strongly connected with the regional ecosystem stability, water conservation as well as climate change.
Kun Feng, Shaoxiu Ma, Haiyang Xi, Linhao Liang, Weiqi Liu, Atsushi Tsunekawa   +5 more
doaj   +1 more source

Synthetic Data Generation for Augmenting Small Samples

Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them ...
Liu, Dan, Kababji, Samer El, Mitsakakis, Nicholas, Pilgram, Lisa, Walters, Thomas, Clemons, Mark, Pond, Greg, El-Hussuna, Alaa, Emam, Khaled El   +8 more
openaire   +2 more sources

Sample adaptive data augmentation with progressive scheduling

Data augmentation is a widely adopted technique utilized to improve the robustness of automatic speech recognition (ASR). Employing a fixed data augmentation strategy for all training data is a common practice. However, it is important to note that there can be variations in factors such as background noise, speech rate, etc.
Lu, Hongxuan, Li, Biao
openaire   +2 more sources

Adaptive Data Augmentation for Thompson Sampling

In linear contextual bandits, the objective is to select actions that maximize cumulative rewards, modeled as a linear function with unknown parameters. Although Thompson Sampling performs well empirically, it does not achieve optimal regret bounds.
openaire   +2 more sources

Method for Recognition of Communication Interference Signals under Small-Sample Conditions

Applied Sciences
To address the difficulty in obtaining a large number of labeled jamming signals in complex electromagnetic environments, this paper proposes a small-sample communication jamming signal recognition method based on WDCGAN-SA (Wasserstein Deep Convolution ...
Rong Ge, Yusheng Li, Yonggang Zhu, Xiuzai Zhang, Kai Zhang, Minghu Chen   +5 more
doaj   +1 more source

Online Distillation with Mixed Sample Augmentation

arXiv.org, 2022
Yiqing Shen, Liwu Xu, Yuzhe Yang, Yaqian Li, Yandong Guo   +4 more
semanticscholar   +1 more source

The Augmented Factorization Bound for Maximum-Entropy Sampling

The maximum-entropy sampling problem (MESP) aims to select the most informative principal submatrix of a prespecified size from a given covariance matrix. This paper proposes an augmented factorization bound for MESP based on concave relaxation. By leveraging majorization and Schur-concavity theory, we demonstrate that this new bound dominates the ...
openaire   +2 more sources

Wear prediction of internal combustion engine cylinder liners based on adaptive generative adversarial networks

Results in Engineering
The cylinder liner is an important component of internal combustion engine, and its wear directly affects the performance and service life of the engines.
Jianxiong Kang, Chang Shu, Haoran Yang, Yaofeng Liu, Changfeng Yan, Feifei Zhang   +5 more
doaj   +1 more source

Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs

The difficulty of multiple-choice questions (MCQs) is a crucial factor for educational assessments. Predicting MCQ difficulty is challenging since it requires understanding both the complexity of reaching the correct option and the plausibility of distractors, i.e., incorrect options.
Wanyong Feng, Peter Tran, Stephen Sireci, Andrew S. Lan   +3 more
openaire   +2 more sources

Imbalanced data classification using graph based transformation

Scientific Reports
Imbalanced data classification is a challenging task in real applications. In this work. A method is proposed for image classification using imbalanced distribution of classes.
Maryam Imani
doaj   +1 more source