  We extend and improve the work of Model Agnostic Anchors for explanations on
image classification through the use of generative adversarial networks (GANs).
Using GANs, we generate samples from a more realistic perturbation
distribution, by optimizing under a lower dimensional latent space. This
increases the trust in an explanation, as results now come from images that are
more likely to be found in the original training set of a classifier, rather
than an overlay of random images. A large drawback to our method is the
computational complexity of sampling through optimization; to address this, we
implement more efficient algorithms, including a diverse encoder. Lastly, we
share results from the MNIST and CelebA datasets, and note that our
explanations can lead to smaller and higher precision anchors.
