Regularization plays a crucial role in machine learning models, especially for deep neural networks. The existing regularization techniques mainly rely on the i.i.d. assumption and only consider the knowledge from the current sample, without the leverage of the neighboring relationship between samples. In this work, we propose a general regularizer called \textbf{Patch-level Neighborhood Interpolation~(Pani)} that conducts a non-local representation in the computation of networks. Our proposal explicitly constructs patch-level graphs in different layers and then linearly interpolates neighborhood patch features, serving as a general and effective regularization strategy. Further, we customize our approach into two kinds of popular regularization methods, namely Virtual Adversarial Training (VAT) and MixUp as well as its variants. The first derived \textbf{Pani VAT} presents a novel way to construct non-local adversarial smoothness by employing patch-level interpolated perturbations. The second derived \textbf{Pani MixUp} method extends the MixUp, and achieves superiority over MixUp and competitive performance over state-of-the-art variants of MixUp method with a significant advantage in computational efficiency. Extensive experiments have verified the effectiveness of our Pani approach in both supervised and semi-supervised settings.

翻译：正则化在机器学习模型中，尤其是深度神经网络中，扮演着至关重要的角色。现有的正则化技术主要依赖于独立同分布假设，仅考虑当前样本的知识，而未利用样本间的邻域关系。在这项工作中，我们提出了一种名为**片级邻域插值（Pani）** 的通用正则化方法，它在网络计算中引入了非局部表征。我们的方法在不同层显式构建片级图，然后对邻域片特征进行线性插值，从而作为一种通用且有效的正则化策略。此外，我们将该方法定制为两种流行的正则化方法，即虚拟对抗训练（VAT）和MixUp及其变体。第一种派生的**Pani VAT**通过使用片级插值扰动，提出了一种构建非局部对抗平滑性的新颖方式。第二种派生的**Pani MixUp**方法扩展了MixUp，在计算效率上具有显著优势，其性能优于MixUp，并与最先进的MixUp变体方法相比具有竞争力。大量实验验证了我们的Pani方法在监督和半监督场景下的有效性。