Though deep neural networks have achieved impressive success on various vision tasks, obvious performance degradation still exists when models are tested in out-of-distribution scenarios. In addressing this limitation, we ponder that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be properly manipulated to improve the generalization ability of deep learning models. Existing methods commonly consider feature statistics as deterministic values measured from the learned features and do not explicitly model the uncertain statistics discrepancy caused by potential domain shifts during testing. In this paper, we improve the network generalization ability by modeling domain shifts with uncertainty (DSU), i.e., characterizing the feature statistics as uncertain distributions during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. During inference, we propose an instance-wise adaptation strategy that can adaptively deal with the unforeseeable shift and further enhance the generalization ability of the trained model with negligible additional cost. We also conduct theoretical analysis on the aspects of generalization error bound and the implicit regularization effect, showing the efficacy of our method. Extensive experiments demonstrate that our method consistently improves the network generalization ability on multiple vision tasks, including image classification, semantic segmentation, instance retrieval, and pose estimation. Our methods are simple yet effective and can be readily integrated into networks without additional trainable parameters or loss constraints. Code will be released in https://github.com/lixiaotong97/DSU.
翻译:尽管深度神经网络在各种视觉任务上取得了令人印象深刻的成功,但当模型在分配范围外的情景中测试模型时,明显的业绩退化仍然存在。在应对这一限制时,我们认为,包含培训数据域特性的特征统计(平均值和标准偏差)可以适当地加以操纵,以提高深层学习模型的概括性能力。现有方法通常认为特征统计是从所学特点中测量的确定性值,没有明确地模拟测试期间潜在领域变化造成的不确定统计差异。在本文中,我们通过以不确定性模拟域变模式(DSU),即将特征统计定性为培训期间的不确定分布特征。具体地说,我们在考虑潜在不确定性之后,可以对特征统计进行多变性统计,并遵循多种变异性高地分布法。在推论中,我们提出一个以实例为明智的适应性调整战略,能够应对无法预见的变异性变化,并以微不足道的额外成本进一步加强经过培训的模式的概括性能力。我们还可以对一般化错误和隐含的正规化效果进行理论分析,显示我们方法的多重变现性网络的效能,同时进行我们的系统变现能力实验,包括系统变现。