Tradeoffs in Data Augmentation: An Empirical Study

By Raphael Gontijo Lopes, Sylvia J. Smullin, Ekin Dogus Cubuk, Ethan Dyer. 2021

72 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Jason Wei
2. Sarath Chandar
3. Sook Yoon
4. Eduard H. Hovy
5. Alvaro Fuentes
6. Steven Y. Feng
7. Teruko Mitamura
8. Soroush Vosoughi

Citations in the corpus

• A Survey of Data Augmentation Approaches for NLP (2021, background): “A recent work in vision (Gontijo-Lopes et al., 2020) has proposed that affinity (the distributional shift caused by DA) and diversity (the complexity of the augmentation) can predict DA performance, but it is unclear how these results might translate to NLP. Minimal benefit for pretrained models on…”
• A Comprehensive Survey of Image Augmentation Techniques for Deep Learning (2022, methodology): “In addition, the improvement yielded by image augmentation in the context of model generalizability has been quantified using affinity and diversity [104].”