most citedLeveraging generative models to characterize the failure conditions of image classifiers

1 citations · 1 across the 2 of their papers we have counts for

collaborators

3 papers

cs.LG2024

Confidence Calibration of Classifiers with Many Classes

Adrien LeCoz, Stéphane Herbin, Faouzi Adjed

For classification models based on neural networks, the maximum predicted class probability is often used as a confidence score. This score rarely predicts well the probability of…

cs.CV2024

Explaining an image classifier with a generative model conditioned by uncertainty

Adrien LeCoz, Stéphane Herbin, Faouzi Adjed

We propose to condition a generative model by a given image classifier uncertainty in order to analyze and explain its behavior. Preliminary experiments on synthetic data and a cor…

cs.CV2024★ 1 cited

Leveraging generative models to characterize the failure conditions of image classifiers

Adrien LeCoz, Stéphane Herbin, Faouzi Adjed

We address in this work the question of identifying the failure conditions of a given image classifier. To do so, we exploit the capacity of producing controllable distributions of…