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...
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 t...
This indicates that deep-learning data augmentation techniques can improve the efficiency of image tasks and avoid overfitting, which can be used in the study of the digitization of Chinese paintings.Proceedings of SPIEXinquan LuoQingchen NieYinghui WangZhuwen Zhao...
数据增强(Data Augmentation,DA)并不是唯一试图减弱过拟合的研究,许多提高泛化性的策略从网络本身结构出发,希望模型去学习general而不是special的特征,比如整个CNN的发展过程(AlexNet->VGG->ResNet->Inception-V3->DenseNet),还比如一些技术: Dropout: 我感觉这个就是一种bagging的正则化学习思路,强制网络不能只靠少数...
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 ...
Task‑specific augmentation for NLP NLP的任务特异性增强 Self‑supervised learning and data augmentation 自我监督学习和数据扩充 Transfer and multi‑task learning 迁移和多任务学习 AI‑GAs 人工智能生成算法 Conclusion 背景 对图像进行语义保留增强很容易,但在文本领域要做到这一点要困难得多。
What is data augmentation, how does it work and what are its most prominent use cases? Learn everything you need to know about data augmentation techniques for computer vision.
1. Techniques of Graph Data Augmentation 问题定义:G=(A,X),A∈{0,1}n×n,X∈Rn×d,y∈Rn graph data augmentation (GraphDA) 的基本需求是找到一个映射函数fθ:G→G~=(A~,X~)来丰富或更改给定图中的信息。因为属性图具有多信息通道,因此基于属性图的 GraphDA 可以分为: ...
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...
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