Differentiable Image Data Augmentation and Its Applications: A Survey

Jian Shi, Hakim Ghazzai*, Yehia Massoud

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically neural augmentation networks and differentiable augmentation search. Finally, we discuss current challenges of DDA and future research directions.

Original languageEnglish (US)
Pages (from-to)1148-1164
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume46
Issue number2
DOIs
StatePublished - Feb 1 2024

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.

Keywords

  • Computer vision
  • data augmentation
  • differentiability

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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