  Deep Learning has become a very valuable tool in different fields, and no one
doubts the learning capacity of these models. Nevertheless, since Deep Learning
models are often seen as black boxes due to their lack of interpretability,
there is a general mistrust in their decision-making process. To find a balance
between effectiveness and interpretability, Explainable Artificial Intelligence
(XAI) is gaining popularity in recent years, and some of the methods within
this area are used to generate counterfactual explanations. The process of
generating these explanations generally consists of solving an optimization
problem for each input to be explained, which is unfeasible when real-time
feedback is needed. To speed up this process, some methods have made use of
autoencoders to generate instant counterfactual explanations. Recently, a
method called Deep Guided Counterfactual Explanations (DGCEx) has been
proposed, which trains an autoencoder attached to a classification model, in
order to generate straightforward counterfactual explanations. However, this
method does not ensure that the generated counterfactual instances are close to
the data manifold, so unrealistic counterfactual instances may be generated. To
overcome this issue, this paper presents Distribution Aware Deep Guided
Counterfactual Explanations (DA-DGCEx), which adds a term to the DGCEx cost
function that penalizes out of distribution counterfactual instances.
