Quantitative evaluation of white & black box interpretability methods for image classification

Stanchi, Oscar

Título:
Quantitative evaluation of white & black box interpretability methods for image classification
Autor:
Stanchi, Oscar
Colaboradores:
Ronchetti, FrancoDal Bianco, Pedro A.Ríos, Gastón GustavoHasperué, WaldoPuig, DomenecRashwan, Hatem
Temas:
VISIÓN POR COMPUTADORA
En:
Congreso Argentino de Ciencias de la Computación (30mo : 2024 : La Plata, Argentina)
Resumen:
The field of interpretability in Deep Learning faces significant challenges due to the lack of standard metrics for systematically evaluating and comparing interpretability methods. The absence of quantifiable measures impedes practitioners ability to select the most suitable methods and models for their specific tasks. To address this issue, we propose the Pixel Erosion and Dilation Score, a novel metric designed to assess the robustness of model explanations. Our approach involves applying iterative erosion and dilation processes to heatmaps generated by various interpretability methods, thereby using them to hide and show the important regions of a image to the network, allowing for a coherent and interpretable evaluation of model decision-making processes. We conduct quantitative ablation tests using our metric on the ImageNet dataset with both VGG16 and ResNet18 models. The results reveal that our new measure provides a numerical and intuitive means for comparing interpretability methods and models, facilitating more informed decision-making for practitioner.
URL/DOI:
http://sedici.unlp.edu.ar/handle/10915/176288
Palabras clave:
aprendizaje profundo
Medio:
Soporte electrónico
Tipo de documento:
Artículo
Descripción física:
1 archivo (1,37 MB)
Idioma:
Español
Publicación:
, 2024

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