  Fine-grained classification is a particular case of a classification problem,
aiming to classify objects that share the visual appearance and can only be
distinguished by subtle differences. Fine-grained classification models are
often deployed to determine animal species or individuals in automated animal
monitoring systems. Precise visual explanations of the model's decision are
crucial to analyze systematic errors. Attention- or gradient-based methods are
commonly used to identify regions in the image that contribute the most to the
classification decision. These methods deliver either too coarse or too noisy
explanations, unsuitable for identifying subtle visual differences reliably.
However, perturbation-based methods can precisely identify pixels causally
responsible for the classification result. Fill-in of the dropout (FIDO)
algorithm is one of those methods. It utilizes the concrete dropout (CD) to
sample a set of attribution masks and updates the sampling parameters based on
the output of the classification model. A known problem of the algorithm is a
high variance in the gradient estimates, which the authors have mitigated until
now by mini-batch updates of the sampling parameters. This paper presents a
solution to circumvent these computational instabilities by simplifying the CD
sampling and reducing reliance on large mini-batch sizes. First, it allows
estimating the parameters with smaller mini-batch sizes without losing the
quality of the estimates but with a reduced computational effort. Furthermore,
our solution produces finer and more coherent attribution masks. Finally, we
use the resulting attribution masks to improve the classification performance
of a trained model without additional fine-tuning of the model.
