  Image classification models can depend on multiple different semantic
attributes of the image. An explanation of the decision of the classifier needs
to both discover and visualize these properties. Here we present StylEx, a
method for doing this, by training a generative model to specifically explain
multiple attributes that underlie classifier decisions. A natural source for
such attributes is the StyleSpace of StyleGAN, which is known to generate
semantically meaningful dimensions in the image. However, because standard GAN
training is not dependent on the classifier, it may not represent these
attributes which are important for the classifier decision, and the dimensions
of StyleSpace may represent irrelevant attributes. To overcome this, we propose
a training procedure for a StyleGAN, which incorporates the classifier model,
in order to learn a classifier-specific StyleSpace. Explanatory attributes are
then selected from this space. These can be used to visualize the effect of
changing multiple attributes per image, thus providing image-specific
explanations. We apply StylEx to multiple domains, including animals, leaves,
faces and retinal images. For these, we show how an image can be modified in
different ways to change its classifier output. Our results show that the
method finds attributes that align well with semantic ones, generate meaningful
image-specific explanations, and are human-interpretable as measured in
user-studies.
