OpenMixup：面向视觉表征学习的开源Mixup工具库与基准
OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning
AI总结：
针对mixup增强缺乏标准化实现与基准的问题，本文提出首个面向视觉表征学习的开源mixup工具库OpenMixup，构建多数据集基准并提供模块化代码，通过实验总结性能规律，推动相关研究的可复现性与发展。
摘要：
Mixup增强已成为提升深度神经网络（DNNs）泛化能力的常用技术。然而，标准化实现与基准的缺失阻碍了近期研究进展，导致可复现性差、对比不公平以及结论相互矛盾的问题。本文提出OpenMixup——首个面向视觉表征学习的mixup增强代码库与基准。具体而言，我们从头训练了18个具有代表性的mixup基线，并在11个规模、粒度各异的图像数据集上进行了严格评估，覆盖从细粒度场景到复杂非标志性场景的多种情况。我们还开源了模块化代码库，包含一系列主流视觉骨干网络、优化策略和分析工具集，不仅支持基准测试，还能拓展分类任务之外更广泛的mixup应用，如自监督学习和回归任务。通过实验与实证分析，我们获得了关于mixup性能-效率权衡、泛化性和优化行为的观察与见解，进而为不同需求确定了优选方案。据我们所知，OpenMixup已为多项近期研究提供了支持。我们相信这项工作能进一步推动可复现的mixup增强研究，为领域未来进展奠定坚实基础。源代码与用户文档可访问https://github.com/Westlake-AI/openmixup 获取。
英文摘要：
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. We also open-source our modular codebase, including a collection of popular vision backbones, optimization strategies, and analysis toolkits, which not only supports the benchmarking but enables broader mixup applications beyond classification, such as self-supervised learning and regression tasks. Through experiments and empirical analysis, we gain observations and insights on mixup performance-efficiency trade-offs, generalization, and optimization behaviors, and thereby identify preferred choices for different needs. To the best of our knowledge, OpenMixup has facilitated several recent studies. We believe this work can further advance reproducible mixup augmentation research and thereby lay a solid ground for future progress in the community. The source code and user documents are available at \url{https://github.com/Westlake-AI/openmixup}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
