ML Model Coverage Assessment by Topological Data Analysis Exploration

Abstract

The increasing complexity of deep learning models necessitates advanced methods for model coverage assessment, a critical factor for their reliable deployment. In this study, we introduce a novel approach leveraging topological data analysis to evaluate the coverage of a couple dataset & classification model. By using tools from topological data analysis, our method identifies underrepresented regions within the data, thereby enhancing the understanding of both model performances and data completeness. This approach simultaneously evaluates the dataset and the model, highlighting areas of potential risk. We report experimental evidence demonstrating the effectiveness of this topological framework in providing a comprehensive and interpretable coverage assessment. As such, we aim to open new avenues for improving the reliability and trustworthiness of classification models, laying the groundwork for future research in this domain.