Mixup
Definition
mixup generates training examples by linear mixing of example pairs and their labels.
A data augmentation that linearly combines pairs of examples and labels.
Simply explained
How to mix two training tasks and their solutions with the same proportion; this only makes sense for suitable data.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
Two images and their class labels are combined with the same mixing weight.
Limits and pitfalls
- Apply to unsuitably structured or semantically non-mixable data.
Origin & history
Zhang and co-authors published mixup in 2017.
Use in mid-sized companies
- Possible application: A data team checks whether linear mixtures make sense for the available examples and labels. It compares test quality with and without mixup under the same conditions.
Frequently asked questions
What does Mixup mean?
A data augmentation that linearly combines pairs of examples and labels.
Which mistakes should be avoided?
Apply to unsuitably structured or semantically non-mixable data.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: