# GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative Models Source: [GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative Models]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:36:37.771Z Authors: Kurtis Evan David, Harrison Keane, Jun Min Noh Published: 2019-06-01T21:43:45.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()