In the last few years, many works have tried to explain the predictions of
deep learning models. Few methods, however, have been proposed to verify the
accuracy or faithfulness of these explanations. Recently, influence functions,
which is a method that approximates the effect that leave-one-out training has
on the loss function, has been shown to be fragile. The proposed reason for
their fragility remains unclear. Although previous work suggests the use of
regularization to increase robustness, this does not hold in all cases. In this
work, we seek to investigate the experiments performed in the prior work in an
effort to understand the underlying mechanisms of influence function fragility.
First, we verify influence functions using procedures from the literature under
conditions where the convexity assumptions of influence functions are met.
Then, we relax these assumptions and study the effects of non-convexity by
using deeper models and more complex datasets. Here, we analyze the key metrics
and procedures that are used to validate influence functions. Our results
indicate that the validation procedures may cause the observed fragility.

通过验证和放松传统的凸性假设条件，并使用更深的模型和更复杂的数据集，我们研究了影响力函数的脆弱性，并分析了验证方法可能引起脆弱性的原因。