Results 11 to 20 of about 322,902 (281)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Side-scan sonar (SSS) image sample augmentation plays an important role in improving the effect of deep-learning-based underwater target detection.
Yulin Tang +6 more
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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
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Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams
Sensors, 2023 In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue
Yuechen Wang +5 more
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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.
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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 +2 more
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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 +4 more
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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 +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 +4 more
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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 +8 more
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Remote Sensing, 2023 With the widespread application and functional complexity of deep neural networks (DNNs), the demand for training samples is increasing. This elevated requirement also extends to DNN-based SAR object detection.
Yi Kuang +4 more
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