# Learning Instance-Specific Data Augmentations

**Authors:** Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim  
**arXiv:** [2206.00051](https://arxiv.org/abs/2206.00051)  
**Date:** 2022-05-31  
**Summary page:** https://www.summarizepaper.com/fr/paper/2206.00051v1/

## Abstract

Existing data augmentation methods typically assume independence between transformations and inputs: they use the same transformation distribution for all input instances. We explain why this can be problematic and propose InstaAug, a method for automatically learning input-specific augmentations from data. This is achieved by introducing an augmentation module that maps an input to a distribution over transformations. This is simultaneously trained alongside the base model in a fully end-to-end manner using only the training data. We empirically demonstrate that InstaAug learns meaningful augmentations for a wide range of transformation classes, which in turn provides better performance on supervised and self-supervised tasks compared with augmentations that assume input--transformation independence.