ABELE
Adversarial Black box Explainer generating Latent Exemplars
ABELE is a local, model-agnostic explanation method able to overcome the existing limitations of the local approaches by exploiting the latent feature space, learned through an adversarial autoencoder, for the neighborhood generation process.
Language of the library: Python ≥ 3.6
Additional information: Given an image classified by a given black box model, ABELE provides an explanation for the reasons of the proposed classification. The explanation consists of two parts: (i) a set of exemplars and counter-exemplars images illustrating, respectively, instances classified with the same label and with a different label than the instance to explain, which may be visually analyzed to understand the reasons for the classification, and (ii) a saliency map highlighting the areas of the image to explain that contribute to its classification, and areas of the image that push it towards another label. ABELE is an extension of the iLORE.
The software package contains a reference to external public datasets: - mnist - cifar - fashion. Black boxes demonstrated in the library: - RF - Keras CNN - ResNet