Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty estimation. A commonly-used (first-order) influence function can be implemented efficiently as a post-hoc method requiring access only to the gradients and Hessian of the model. For linear models, influence functions are well-defined due to the convexity of the underlying loss function and are generally accurate even across difficult settings where model changes are fairly large such as estimating group influences. Influence functions, however, are not well-understood in the context of deep learning with non-convex loss functions. In this paper, we provide a comprehensive and large-scale empirical study of successes and failures of influence functions in neural network models trained on datasets such as Iris, MNIST, CIFAR-10 and ImageNet. Through our extensive experiments, we show that the network architecture, its depth and width, as well as the extent of model parameterization and regularization techniques have strong effects in the accuracy of influence functions. In particular, we find that (i) influence estimates are fairly accurate for shallow networks, while for deeper networks the estimates are often erroneous; (ii) for certain network architectures and datasets, training with weight-decay regularization is important to get high-quality influence estimates; and (iii) the accuracy of influence estimates can vary significantly depending on the examined test points. These results suggest that in general influence functions in deep learning are fragile and call for developing improved influence estimation methods to mitigate these issues in non-convex setups.
翻译:对于线性模型来说,影响功能由于基本损失功能的混杂性而定义明确,甚至在模型变化规模大如估计群体影响等困难环境中,一般都非常精确。但影响功能在与非康韦克斯损失功能的深层次学习中并没有很好地理解。在本文件中,我们提供了一个全面而大规模的实验性研究,研究在以Iris、MNIST、CIFAR-10和图像网络等数据集培训的神经网络模型中成功和失败影响功能的经验性模型。通过我们的广泛实验,我们表明网络结构、深度和广度以及模型参数化和正规化技术在影响作用精确度方面影响很大。我们特别发现(i) 影响对浅层次损失功能的深度学习和损失功能的深入学习,我们提供了对神经网络模型成功和功能失败的全面和大规模的经验性研究。(i) 更深层次的网络评估,对于更深层次的网络影响,我们发现这些网络的精确性影响是相当准确的(i) ; 更深层次的网络评估,对于更深层次的测测测测测的网络,其程度是相当的测测测测测测的。