Counterfactuals are a popular framework for interpreting machine learning
predictions. These what if explanations are notoriously challenging to create
for computer vision models: standard gradient-based methods are prone to
produce adversarial examples, in which imperceptible modifications to image
pixels provoke large changes in predictions. We introduce a new,
easy-to-implement framework for counterfactual images that can flexibly adapt
to contemporary advances in generative modeling. Our method, Counterfactual
Attacks, resembles an adversarial attack on the representation of the image
along a low-dimensional manifold. In addition, given an auxiliary dataset of
image descriptors, we show how to accompany counterfactuals with feature
attribution that quantify the changes between the original and counterfactual
images. These importance scores can be aggregated into global counterfactual
explanations that highlight the overall features driving model predictions.
While this unification is possible for any counterfactual method, it has
particular computational efficiency for ours. We demonstrate the efficacy of
our approach with the MNIST and CelebA datasets.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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