Results 1 to 10 of about 116,076 (264)

Learning Sample-Specific Policies for Sequential Image Augmentation

Proceedings of the 29th ACM International Conference on Multimedia, 2021
This paper presents a policy-driven sequential image augmentation approach for image-related tasks. Our approach applies a sequence of image transformations (e.g., translation, rotation) over a training image, one transformation at a time, with the augmented image from the previous time step treated as the input for the next transformation.
Pu Li, Xiaobai Liu, Xiaohui Xie
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Sample size of the reference sample in a case‐augmented study

Pharmacoepidemiology and Drug Safety, 2017
AbstractThe case‐augmented study, in which a case sample is augmented with a reference (random) sample from the source population with only covariates information known, is becoming popular in different areas of applied science such as pharmacovigilance, ecology, and econometrics.
Palash Ghosh, Anup Dewanji
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Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
Crackles are explosive breathing patterns caused by lung air sacs filling with fluid and act as an indicator for a plethora of pulmonary diseases. Clinical studies suggest a strong correlation between the presence of these adventitious auscultations and mortality rate, especially in pediatric patients, underscoring the importance of their pathological ...
Annapurna Kala, Mounya Elhilali
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Augmented Leverage Score Sampling with Bounds

2016
We introduce a modification to the well studied leverage score sampling algorithm which takes into account data scale, called the augmented leverage score, and introduce an initial error bound proof for the case of deterministic sampling – which to our knowledge is the first bound for this augmented leverage score.
Daniel J. Perry, Ross T. Whitaker
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The Method of Augmented Sampling

Applied Statistics, 1968
In this paper we present a new sampling scheme called the method of augmented sampling. The scheme is essentially a two sample procedure and is useful in surveys where all except one of the variates are similarly distributed and the distribution of the single variate differs markedly from the others.
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MantarayAR: Leveraging augmented reality to teach probability and sampling

Computers & Education, 2020
Abstract Drawing from the scholarly literature, it seems that across all levels of education, teachers frequently must assume that students come to them without knowledge of statistics; in essence, instructors start from scratch each time they set out to teach statistics.
Quincy Conley, Robert K. Atkinson, Frank Nguyen, Brian C. Nelson   +3 more
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SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Data augmentation (DA) has been extensively studied to facilitate model optimization in many tasks. Prior DA works focus on designing augmentation operations themselves, while leaving selecting suitable samples for augmentation out of consideration. This might incur visual ambiguities and further induce training biases.
Shiqi Lin, Zhizheng Zhang 0004, Xin Li 0082, Zhibo Chen 0001   +3 more
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Multilabel Sample Augmentation-Based Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2020
The quantity and quality of training samples have a great influence on the performance of most hyperspectral image classification approaches. However, in a real scenario, manually annotating a large number of accurate training samples is extremely labor-intensive and time-consuming.
Qiaobo Hao, Shutao Li 0001, Xudong Kang
openaire   +1 more source

Denoising and Augmented Negative Sampling for Collaborative Filtering

ACM Transactions on Recommender Systems
Negative sampling plays a crucial role in implicit-feedback-based collaborative filtering, where it leverages massive unlabeled data to generate negative signals for guiding supervised learning. The current state-of-the-art approaches focus on utilizing hard negative samples that contain more information to establish a better decision boundary.
Yuhan Zhao 0001, Rui Chen 0012, Riwei Lai, Qilong Han, Hongtao Song, Li Chen 0009   +5 more
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Prototype Augmentation with Dummy Samples

2022 26th International Conference on Pattern Recognition (ICPR), 2022
Hong Yu, Fanzhang Li
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