论文标题
Lumix：通过更好的建模标签不确定性改善混合
LUMix: Improving Mixup by Better Modelling Label Uncertainty
论文作者
论文摘要
当接受嘈杂的样本和正则化技术训练时，现代深层网络可以更好地推广。事实证明，混合和cutmix可有效地增加数据，以避免过度拟合。以前的基于混合的方法线性结合图像和标签以生成其他训练数据。但是，如果对象不占据整个图像，那么这是有问题的。正如我们在图1中所示。正确分配标签权重甚至对于人类而言也很难，并且没有明确的标准来测量它。为了解决这个问题，在本文中，我们提出了Lumix，该文章通过在训练过程中添加标签扰动来对这种不确定性进行建模。 Lumix很简单，因为它只能在几行代码中实现，并且可以普遍应用于任何深层网络\ EG CNN和视觉变压器，并以最低的计算成本应用。广泛的实验表明，我们的Lumix可以始终提高Imagenet上多样性和容量的网络的性能，对于小型DEIT-S，对于大型XCIT-L的小型DEIT-S和$+0.6 \％$。我们还证明，在对Imagenet-O和Imagenet-A进行评估时，Lumix可以带来更好的鲁棒性。可以找到源代码\ href {https://github.com/kevin-ssy/lumix} {tere}
Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentation to help avoid overfitting. Previous Mixup-based methods linearly combine images and labels to generate additional training data. However, this is problematic if the object does not occupy the whole image as we demonstrate in Figure 1. Correctly assigning the label weights is hard even for human beings and there is no clear criterion to measure it. To tackle this problem, in this paper, we propose LUMix, which models such uncertainty by adding label perturbation during training. LUMix is simple as it can be implemented in just a few lines of code and can be universally applied to any deep networks \eg CNNs and Vision Transformers, with minimal computational cost. Extensive experiments show that our LUMix can consistently boost the performance for networks with a wide range of diversity and capacity on ImageNet, \eg $+0.7\%$ for a small model DeiT-S and $+0.6\%$ for a large variant XCiT-L. We also demonstrate that LUMix can lead to better robustness when evaluated on ImageNet-O and ImageNet-A. The source code can be found \href{https://github.com/kevin-ssy/LUMix}{here}
