Explaining an image classifier with a generative model conditioned by uncertainty
Résumé
Identifying sources of uncertainty in an image classifier is a
crucial challenge. Indeed, the decision process of those models is opaque
and does not necessarily correspond to what we might expect. To help
characterize classifiers, generative models can be used as they allow the
control of visual attributes. Here we use a generative adversarial network
to generate images corresponding to how a classifier sees the image. More
specifically, we consider the classifier maximum softmax probability as
an uncertainty estimation and use it as an additional input to condition
the generative model. This allows us to generate images that result in
uncertain predictions, giving us a global view of which images are harder
to classify. We can also increase the uncertainty of a given image and
observe the impact of an attribute, providing a more local understanding
of the decision process. We perform experiments on the MNIST dataset,
augmented with corruptions. We believe that generative models are a
helpful tool to explain the behavior and uncertainties of image classifiers
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