The audio signals are conveniently processed to generate mel spectrograms, which are used as inputs to the deep neural network architecture. This paper proposes a selected set of data augmentation techniques that allow to reduce the network overfitting. We achieve an average recognition accuracy of 86.44% on publicly distributed databas...
最近,在[Cheung and Yeung,2021]中提出了嵌入空间中的另一种数据增强方法,名为MODALS(Modalityagnostic Automated data augmentation in the Latent space)。MODALS方法不是训练自动编码器来学习潜在空间并生成额外的合成数据用于训练,而是联合潜在空间增强的不同组成来训练分类模型,并证明了这种方法在时间序列分类问题的...
Back‑translation augmentation: 从一个语言翻译到另一个语言作为数据增强。 Style augmentation:一种利用深度网络来增强数据以训练其他深度网络的增强策略。这是一种有趣的策略,可以防止过度拟合高频特征或模糊语言形式,例如专注于意义。在文本数据域中,这可以描述将一位作者的写作风格转移到另一位作者的写作风格,以用...
In this section, we present some basic but powerful augmentation techniques that are popularly used. Before we explore these techniques,for simplicity, let us makeone assumption. The assumption is that,we don’t need to consider what lies beyond the image’s boundary. We’ll use the below te...
2.2Population Based AugmentationAutoAugment计算成本非常高昂，伯克利AI研究院提出的Population Based ...
方法4：数据增强（Data Augmentation）方法 5：先验知识 其他野路子：对抗生成网络（GAN）, 级联分类器（...
2023, Computers and Electronics in Agriculture Citation Excerpt : In our study, we used several data augmentation techniques to augment the training set and improve the robustness of our DCA-YOLOv8 model for cattle detection. We applied random horizontal and vertical flipping, random cropping, random...
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summar
Global data augmentation techniques are used when the point clouds in a dataset have little variation. A global technique applies a transformation to the entire point cloud to generate new samples of the point cloud that are not present in the original data set. The same transformation is a...
(1)人工增加训练集的大小. 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.也就是Data Augmentation (2)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-paramete...