In this work, we study the challenge of providing human-understandable
descriptions for failure modes in trained image classification models. Existing
works address this problem by first identifying clusters (or directions) of
incorrectly classified samples in a latent space and then aiming to provide
human-understandable text descriptions for them. We observe that in some cases,
describing text does not match well with identified failure modes, partially
owing to the fact that shared interpretable attributes of failure modes may not
be captured using clustering in the feature space. To improve on these
shortcomings, we propose a novel approach that prioritizes interpretability in
this problem: we start by obtaining human-understandable concepts (tags) of
images in the dataset and then analyze the model's behavior based on the
presence or absence of combinations of these tags. Our method also ensures that
the tags describing a failure mode form a minimal set, avoiding redundant and
noisy descriptions. Through several experiments on different datasets, we show
that our method successfully identifies failure modes and generates
high-quality text descriptions associated with them. These results highlight
the importance of prioritizing interpretability in understanding model
failures.

我们研究了在训练图像分类模型中提供人类可理解的失败模式描述的挑战。通过研究现有方法的局限性，我们提出了一种新颖的方法，首先从数据集中获取人类可理解的图像概念（标签），然后根据这些标签的组合的存在与否分析模型的行为，以提高对失败模式的识别和生成与之相关的高质量文本描述的能力，这表明在理解模型失败中优先考虑可解释性的重要性。