Feature space DA approaches generate augmented examples in the model’s feature space rather than input data.

University of Michigan - Ann Arbor

Rule-based data augmentation is to apply basic changes to the dataset which enlarges the divergence that the neural network sees.

Rule-based Data Augmentation

https://arxiv-download.xixiaoyao.cn/pdf/2105.03075.pdf

A Survey of Data Augmentation Approaches for NLP

Feature Space DA

It's a set of token-level random perturbation operations including random insertion, deletion, and swap. It shows improved performance on many text classification tasks. 

Easy Data Augmentation (EDA)

UDA show how supervised DA methods can be exploited for unsupervised data through consistency training on $(x, DA(x))$ pairs.

Unsupervised Data Augmentation (UDA)

For dependency-annotated sentences, children of the same parent are swapped or some deleted.

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