Résumé
Counterfactual explanations are an emerging tool to enhance interpretability
of deep learning models. Given a sample, these methods seek to find and display
to the user similar samples across the decision boundary. In this paper, we
propose a generative adversarial counterfactual approach for satellite image
time series in a multi-class setting for the land cover classification task.
One of the distinctive features of the proposed approach is the lack of prior
assumption on the targeted class for a given counterfactual explanation. This
inherent flexibility allows for the discovery of interesting information on the
relationship between land cover classes. The other feature consists of
encouraging the counterfactual to differ from the original sample only in a
small and compact temporal segment. These time-contiguous perturbations allow
for a much sparser and, thus, interpretable solution. Furthermore,
plausibility/realism of the generated counterfactual explanations is enforced
via the proposed adversarial learning strategy.