Before revision:  FF-NSL uses a pre-trained neural network to extract symbolic facts from unstructured data and an ILP system to learn a hypothesis that performs a downstream classification task. In order to evaluate the applicability of our approach to real-world applications, the framework is evaluated on tasks where distributional shiftsare introduced to unstructured input data , for which pre-trained neural networks are likely to predict incorrectly and with high confidence.
After revision:  To demonstrate the generality and robustness of FF-NSL, we use two datasets subject to distributional shifts , for which pre-trained neural networks are likely to predict incorrectly and with high confidence.