Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the representation properties of DL have been well studied, uncertainty quantification remains challenging and largely unexplored. Data augmentation techniques are a natural approach...
Augmentation with Gaussian Processes Augmentation with Deep Generative Models 相关开源实现 总结 前言 最近,深度学习在许多时间序列分析任务中表现出色。深度神经网络的优越性能严重依赖于大量训练数据以避免过度拟合。然而,许多现实世界时间序列应用的标记数据可能受到限制,例如医学时间序列中的分类和AIOps中的异常检测。作...
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 [...
For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task...
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是使用记忆增强网络。Zero-shot learning是一种更极端的模式,在这种模式...
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。
In this paper, we have focused on the most frequently mentioned problem in the field of machine learning, that is the lack of sufficient amount of the training data or uneven class balance within the datasets. One of the ways of dealing with this problem is so called data augmentation. In...
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...
Learning and inference of layer-wise model parameters are implemented in a Bayesian setting. By exploring the idea of data augmentation and introducing auxiliary Polya-Gamma variables, simple and efficient Gibbs sampling and mean-field variational Bayes (VB) inference are implemented. To address large...
Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ...