Data Augmentation:通过平移、 翻转、加噪声等方法从已有数据中创造出一批“新”的数据,人工增加训练集的大小。 Regularization:数据量比较小会导致模型过拟合, 使得训练误差很小而测试误差特别大. 通过在Loss Function 后面加上正则项可以抑制过拟合的产生。缺点是引入了一个需要手动调整的hyper-parameter。 Dropout:这...
Obara, B.: Style Augmentation: Data Augmentation via Style Randomization. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, June 16-20, 2019. pp. 83–92. Computer Vision Foundation / IEEE (2019) ...
Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness...
Data augmentationin data analysis are techniques used to increase the amount or diversity of data by adding slightly modified(e.g. random (but realistic) transformations such as image rotation) copies of already existing data or newly created synthetic data from existing data. It acts as aregulari...
However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, ...
Moreover, we summarize the applications of graph data augmentation in two representative problems in data-centric deep graph learning: (1) reliable graph learning which focuses on enhancing the utility of input graph as well as the model capacity via graph data augmentation; and (2) low-resource...
DL model. At the same time, data augmentation can be used to reduce the probability of overfitting and increase model generalisability. In contrast to the techniques listed above for improving model generalisation, data Augmentation addresses overfitting from the source of the problem (i.e.the ...
Finally, we explored the use of a simulated dataset as data augmentation to replace the real dataset for training a neural network. As shown in Table3, using the same real test set initially separated out from the training, we compared the performance of the model trained on the whole traini...
data augmentation using thesaurus: https://arxiv.org/pdf/1509.01626.pdf https://theneuralperspective.com/ http://casa.disi.unitn.it/~moschitt/since2013/2015_SIGIR_Severyn_TwitterSentimentAnalysis.pdf https://einstein.ai/research/state-of-the-art-deep-learning-model-for-question-answering https:...
MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysisarxiv.org/abs/2306.17466 论文简要: 数据增强(DA)已被广泛应用于计算机视觉领域,以缓解数据短缺问题,而在医学图像分析(MIA)中的数据增强面临着多重挑战。 MIA 中普遍采用的数据增强方法包括通用数据增强和生成对抗网络(GAN)数...