Results 11 to 20 of about 325,176 (282)

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   +6 more sources

Self-supervised Action Recognition Based on Skeleton Data Augmentation and Double Nearest Neighbor Retrieval [PDF]

Jisuanji kexue, 2023
Traditional self-supervised methods based on skeleton data often take different data augmentation of a sample as positive examples,and the rest of the samples are regarded as negative examples,which makes the ratio of positive and negative samples ...
WU Yushan, XU Zengmin, ZHANG Xuelian, WANG Tao
doaj   +1 more source

Classification of Multiwavelength Transients with Machine Learning [PDF]

, 2021
With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume.
Bassett, Bruce A., Fender, R., Lahav, O., Lochner, M., McEwen, J. D., Peiris, H. V., Pietka, M., Sooknunan, K., Stewart, A. J., Woudt, P. A.   +9 more
core   +2 more sources

Sequence-Level Mixed Sample Data Augmentation [PDF]

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
EMNLP ...
Guo, Demi, Kim, Yoon, Rush, Alexander M.
openaire   +2 more sources

A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model

Journal of Marine Science and Engineering, 2023
The limited number and under-representation of side-scan sonar samples hinders the training of high-performance underwater object detection models. To address this issue, in this paper, we propose a diffusion model-based method to augment side-scan sonar
Zhiwei Yang, Jianhu Zhao, Hongmei Zhang, Yongcan Yu, Chao Huang   +4 more
doaj   +1 more source

Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation

Algorithms, 2022
Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies. The existing work focuses on image classification and object detection, whereas we provide the first study on semantic image ...
Misgana Negassi, Diane Wagner, Alexander Reiterer   +2 more
openaire   +4 more sources

Augmented Negative Sampling for Collaborative Filtering

Proceedings of the 17th ACM Conference on Recommender Systems, 2023
Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary.
Yuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song, Li Chen   +5 more
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Mixed Sample Augmentation for Online Distillation

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks. Previous empirical analysis has illustrated an orthogonal performance gain between MSR and conventional offline Knowledge Distillation (KD).
Shen, Yiqing, Xu, Liwu, Yang, Yuzhe, Li, Yaqian, Guo, Yandong   +4 more
openaire   +2 more sources

Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)

GIScience & Remote Sensing, 2023
Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing.
Yunze Zang, Yuean Qiu, Xuehong Chen, Jin Chen, Wei Yang, Yifei Liu, Longkang Peng, Miaogen Shen, Xin Cao   +8 more
doaj   +1 more source

A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments

, 2022
Data-Augmentation (DA) is known to improve performance across tasks and datasets. We propose a method to theoretically analyze the effect of DA and study questions such as: how many augmented samples are needed to correctly estimate the information encoded by that DA? How does the augmentation policy impact the final parameters of a model?
Balestriero, Randall, Misra, Ishan, LeCun, Yann   +2 more
openaire   +2 more sources