1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…
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…
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models
Adrien LeCoz, Houssem Ouertatani, Stéphane Herbin +1
Image classifiers should be used with caution in the real world. Performance evaluated on a validation set may not reflect performance in the real world. In particular, classifiers…