论文标题
Randomix：一种混合样本数据增强方法，具有多种混合模式
RandoMix: A mixed sample data augmentation method with multiple mixed modes
论文作者
论文摘要
数据增强在增强各个领域的机器学习模型的鲁棒性和性能方面起着至关重要的作用。在这项研究中，我们介绍了一种称为Randomix的新型混合样本数据增强方法。 Randomix专门设计用于同时解决鲁棒性和多样性挑战。它利用了线性和混合模式的组合，在候选选择和重量调整方面引入了灵活性。我们评估Randomix对包括CIFAR-10/100，Tiny-Imagenet，ImageNet和Google语音命令在内的不同数据集的有效性。我们的结果表明，与现有技术（例如混合，cutmix，fmix和resizemix）相比，其性能优越。值得注意的是，Randomix在增强对抗性噪声，自然噪声和样品阻塞的模型鲁棒性方面表现出色。全面的实验结果和对参数调整的见解强调了Randomix作为一种多功能有效的数据增强方法的潜力。此外，它无缝集成到训练管道中。
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask-mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior performance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline.
