Contents
How does data augmentation work for unstructured data?
For unstructured data such as images and text, the augmentation techniques vary from simple transformations to neural network generated data, based on the complexity of the application. The augmentation techniques for images and text type data are discussed separately in the following sections.
How is data augmentation used in deep learning?
Numerical Data Augmentation The augmentation techniques used in deep learning applications depends on the type of the data. To augment plain numerical data, techniques such as SMOTE or SMOTE NC are popular. These techniques are generally used to address the class imbalance problem in classification tasks.
How is data augmentation used in text classification?
The fine-tuned models are used for augmented data generation. The code associated with this method is available on varying-Github. Some of the other data augmentation techniques that are effectively used for text classification purposes are; Random Insertion, Random swap, and Random deletion.
How are data augmentation techniques used in computer vision?
The above Augmentation techniques help in generalizing the model by preventing the overfitting and in turn increases the accuracy of the model. These techniques can be applicable only for the Computer Vision problems with image datasets. There are also techniques to generate synthetic data for other types of datasets also.
How are data augmentation techniques used in deep learning?
Five Popular Data Augmentation techniques. Below are some of the most popular data augmentation widely used in deep learning. Random Rotation. Flip (Horizontal and Vertical). Zoom. Random Shift. Brightness. To get a better understanding of these data augmentation techniques we are going to use a cat image.
How is data augmentation used in computer vision?
While the use of augmented data in computer vision applications is very popular and standardized, the data augmentation techniques in NLP applications are still in the exploratory phase. This is largely due to the complexity involved in language processing.
What is the difference between deterministic and stochastic data augmentation?
For each Data Augmentation, we will define two variants of it, a stochastic one and a deterministic one. In the stochastic one, the augmentation happens randomly, whereas in deterministic, the parameters of the augmentation (like the angle to be rotated are held fixed). This article will outline the general approach to writing an augmentation.