Methods › Computer Vision › Adversarial Image Data Augmentation › MaxUp

MaxUp

2 papers tagged archive 2025-07-28

Introduced by Chengyue Gong et al. in MaxUp: A Simple Way to Improve Generalization of Neural Network Training

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

MaxUp is an adversarial data augmentation technique for improving the generalization performance of machine learning models. The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data. By doing so, we implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. For example, in the case of Gaussian perturbation, MaxUp is asymptotically equivalent to using the gradient norm of the loss as a penalty to encourage smoothness.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification2
Language Modeling2
Language Modelling2
image-classification2
Data Augmentation1
Few-Shot Image Classification1
General Classification1

Usage over time archive 2025-07-28

Papers per year tagged with MaxUp: 2020 to 2021, peak 1 1 0 2020: 1 paper 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Adversarial Image Data Augmentation

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