While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in computer vision (CV), several studies have also shown their limitations and defects. In particular, CV models often make systematic errors on important subsets of data called slices , which are groups of data sharing a set of attributes. A slice discovery method (SDM) is meant to detect semantically meaningful slices on which the model performs poorly, called rare slices . We propose a modular neurosymbolic SDM whose distinctive advantage is the extraction via inductive logic programming of human-readable logical rules describing rare slices, and thus enhancing the explainability of CV models. To this end, a methodology for inducing the occurrence of rare slices in a model is presented. We validate the SDM approach on both the synthetic Super-CLEVR and real-world ImageNet datasets. Our experiments demonstrate the complete pipeline: first, we successfully induce targeted rare slices using our taxonomy-based heuristic; second, our neurosymbolic SDM correctly identifies these slices and produces precise, human-readable logical rules to describe them; and finally, these rules are used to guide a data augmentation process that successfully mends model behaviour and improves its predictive performance. 1
Collevati et al. (Sun,) studied this question.