OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning

Understand

Mixup augmentation has emerged as a widely used technique for improving the generalization ability of deep neural networks (DNNs).

• However, the lack of standardized implementations and benchmarks has impeded recent progress, resulting in poor reproducibility, unfair comparisons, and conflicting insights.
• In this paper, we introduce OpenMixup, the first mixup augmentation codebase, and benchmark for visual representation learning.
• Specifically, we train 18 representative mixup baselines from scratch and rigorously evaluate them across 11 image datasets of varying scales and granularity, ranging from fine-grained scenarios to complex non-iconic scenes.