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...
Data augmentation can increase the variety of training samples and prevent overfitting. Similar concepts have been leveraged in the fields of deep learning in recent years, and there are many existing methods in the literature that prove to improve the performance of various neural nets. In AlexNet...
google了一下deep learning data augmentation 发现了github几种开源的的方法主要是使用opencv结合python的PIL库。最终发现Augmentor好用 本文内容如下: 传统的opencv结合python的multiprocessing任务队列旋转生成图片 使用Augmentor生成样本 先上几张生成的图片看下效果: 原始图片 旋转生成: Augmentor 生成 下面贴出代码,...
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...
[3] Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, Furao Shen. Image Data Augmentation for Deep Learning: A Survey. [4]Daochen Zha,Zaid Pervaiz Bhat,Kwei-Herng Lai,Fan Yang,Zhimeng Jiang,Shaochen Zhong,Xia Hu. Data-centric Artificial Intelligence: A Survey. ...
在深度学习中,为了避免出现过拟合(Overfitting),通常我们需要输入充足的数据量。当数据量不够大时候,常常采用以下几种方法: 1. 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。 2. Regularization
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 summarized into strengthening local decision boundarie...
This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. This is Part 2 of How to use Deep Learning when you have Limited Data. Checkout Part 1here. We have all been there. You have a stellar concept that can be implemented using a ma...
glycemia. We tackle these two challenges using transfer learning and data augmentation, respectively. We systematically examined three neural network architectures, different loss functions, four transfer-learning strategies, and four data augmentation techniques, including mixup and generative models. Taken ...
The increasingly popular adoption of deep learning models in many critical source code tasks motivates the development of data augmentation (DA) techniques to enhance training data and improve various capabilities (e.g., robustness and generalizability) of these models. Although a series of DA ...