ML Model Coverage Assessment by Topological Data Analysis Exploration - IRT SystemX
Communication Dans Un Congrès Année : 2024

ML Model Coverage Assessment by Topological Data Analysis Exploration

Ayman Fakhouri
  • Fonction : Auteur
  • PersonId : 1420974
Faouzi Adjed
Martin Gonzalez
Martin Royer
  • Fonction : Auteur
  • PersonId : 1420975

Résumé

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.
Fichier principal
Vignette du fichier
AAAI_TDA_Coverage_author_version.pdf (2.28 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04717675 , version 1 (03-10-2024)

Identifiants

  • HAL Id : hal-04717675 , version 1

Citer

Ayman Fakhouri, Faouzi Adjed, Martin Gonzalez, Martin Royer. ML Model Coverage Assessment by Topological Data Analysis Exploration. ATRACC workshop 2024 - AI Trustworthiness and Risk Assessment for Challenged Contexts / AAAI 2024 Fall Symposium, Nov 2024, Arlington (VA), United States. ⟨hal-04717675⟩
0 Consultations
0 Téléchargements

Partager

More