Tian Chris Xing, Li Haoliang, Xie Xiaofei, Liu Yang, Wang Shiqi
IEEE Trans Pattern Anal Mach Intell. 2023 Jan;45(1):1302-1311. doi: 10.1109/TPAMI.2022.3157441. Epub 2022 Dec 5.
This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e., misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples. As such, the decision behavior of the DNN is optimized, avoiding the arbitrary neurons that are deleterious for the unseen samples, and leading to the trained DNN that can be better generalized to out-of-distribution samples. Extensive studies on various domain generalization tasks based on both single and multiple domain(s) setting demonstrate the effectiveness of our proposed approach compared with state-of-the-art baseline methods. We also analyze our method by conducting visualization based on network dissection. The results further provide useful evidence on the rationality and effectiveness of our approach.
本文聚焦于领域泛化任务，在此任务中领域知识不可用，更糟糕的是，训练期间只能使用来自单个领域的样本。我们的动机源于深度神经网络（DNN）测试的最新进展，该进展表明最大化DNN的神经元覆盖率有助于探索DNN可能存在的缺陷（即错误分类）。更具体地说，通过将DNN视为一个程序，并将每个神经元视为代码的一个功能点，在网络训练期间，我们旨在通过利用原始样本和增强样本之间的梯度相似性正则化来最大化DNN的神经元覆盖率，从而提高泛化能力。这样，DNN的决策行为得到优化，避免了对未见样本有害的任意神经元，从而使训练后的DNN能够更好地泛化到分布外的样本。基于单领域和多领域设置对各种领域泛化任务进行的广泛研究表明，与现有最先进的基线方法相比，我们提出的方法是有效的。我们还通过基于网络剖析的可视化来分析我们的方法。结果进一步为我们方法的合理性和有效性提供了有用的证据。
