Data augmentation and synthetic data generation are distinct yet complementary techniques in machine learning: Augmented data: This involves creating modified versions of existing data to increase dataset diversity. For example, in image processing, applying transformations like rotations, flips, or color ...
2、Data Augmentation Methods in NLP 作者根据生成样本的多样性程度,将NLP中数据增强方法分为了以下三种: Paraphrasing:对句子中的词、短语、句子结构做一些更改,保留原始的语义。生成与原始数据语义差异有限的增强数据。增强的数据传递的信息与原始形式非常相似。 Noising:在保证label不变的同时,在文本上增加一些离散或...
预训练支持使用大数据集初始化权重,同时仍然支持网络架构设计的灵活性。 ⑤ One-shot and Zero-shot learning 。One-shot learning通常用于面部识别应用。 一次性学习的一种方法是使用siamese 网络,该网络学习距离函数,这样即使网络仅在一个或几个实例上进行过训练,图像分类也是可能的。另一种非常流行的一次性学习方法...
论文翻译:A Group-Theoretic Framework for Data Augmentation 最近发现data augmentation已经有了一些理论工作,早一点的有ICML上的kernel theory。而今天要解读的是使用群理论进行分析的一篇文章。 摘要 数据增强在训练神经网络时被广泛使用:在训练集中除了原始数据还有被适度转换的数据。然而,据我们所知,用来解释数据增强...
1.2 Structure-wise Augmentation 分为四种方法: edge addition/dropping node addition/dropping graph diffusion graph sampling 1.2.1 Edge Addition/Dropping 即 保留原始节点顺序,对邻接矩阵种的元进行改写。 基于图稀疏性(graph sparsification)的图结构优化方法 [8、9],基于图结构整洁性(graph sanitation)的方法 [...
2.1 Data Augmentations based on basic image manipulations 2.2 Geometric versus photometric transformations 2.3 Data Augmentations based on Deep Learning 3. Design considerations for image Data Augmentation C. Shorten and T. M. Khoshgoftaar, ‘A survey on Image Data Augmentation for Deep Learning’,...
Implementing an enterprise data warehouse (EDW) can be a great way to support your digital transformation journey. According to a Gartner survey, 72 percent of data and analytics leaders at enterprises are leading or involved in digital transformation initiatives. However, hiring data leaders isn’t...
SpecAugment是一种log梅尔声谱层面上的数据增强方法,可以将模型训练的过拟合问题转化为欠拟合问题,以便通过大网络和长时训练策略来缓解欠拟合问题,提升语音识别效果 模型: 输入特征:Fbank特征 声谱增强:将log梅尔声谱的时域和频域看作二维图像,时间片长度为τ,频域长度ν ...
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. How...
2. Applications of Graph Data Augmentationin Deep Graph Learning GraphDA 主要是为了 optimal graph learning 和 low-resourcegraph learning 问题。 2.1 GraphDA for Optimal Graph Learning 分为两级:Optimal Structure Learning,Optimal Feature Learning