Explanation  by Progressive  Exaggeration
As machine learning methods see greater adoption and implementation in high stakes applications suchas medical image diagnosis, the need for model interpretability and explanation has become morecritical.Classical approaches that assess feature importance do not explain how and why a particular region of an image is relevant to the prediction.We proposea method that explains the outcome of a classification black-box by gradually exaggeratingthe semantic effect of a given class.Given a query input to a classifier, our method produces aprogressive set of plausible variations of that query, which gradually change the posterior probabilityfrom its original class to its negation.These counter-factually generated samples preserve featuresunrelated to the classification decision, such that a user can employ our method as a tuning knob to traverse a data manifold while crossing the decision boundary. ", Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.
A method to explain a classifier, by generating visual perturbation of an image by exaggerating or diminishing the semantic features that the classifier associates with a target label.A model that when given a query input to a black-box, aims to explain the outcome by providing plausible and progressive variations to the query that can result in a change to the output.A method for explaining the output of black box classification of images, that generates gradual perturbation of outputs in response to gradually perturbed input queries.