Faster ISNet for Background Bias Mitigation on Deep Neural Networks
CoRR(2024)
摘要
Image background features can constitute background bias (spurious
correlations) and impact deep classifiers decisions, causing shortcut learning
(Clever Hans effect) and reducing the generalization skill on real-world data.
The concept of optimizing Layer-wise Relevance Propagation (LRP) heatmaps, to
improve classifier behavior, was recently introduced by a neural network
architecture named ISNet. It minimizes background relevance in LRP maps, to
mitigate the influence of image background features on deep classifiers
decisions, hindering shortcut learning and improving generalization. For each
training image, the original ISNet produces one heatmap per possible class in
the classification task, hence, its training time scales linearly with the
number of classes. Here, we introduce reformulated architectures that allow the
training time to become independent from this number, rendering the
optimization process much faster. We challenged the enhanced models utilizing
the MNIST dataset with synthetic background bias, and COVID-19 detection in
chest X-rays, an application that is prone to shortcut learning due to
background bias. The trained models minimized background attention and hindered
shortcut learning, while retaining high accuracy. Considering external
(out-of-distribution) test datasets, they consistently proved more accurate
than multiple state-of-the-art deep neural network architectures, including a
dedicated image semantic segmenter followed by a classifier. The architectures
presented here represent a potentially massive improvement in training speed
over the original ISNet, thus introducing LRP optimization into a gamut of
applications that could not be feasibly handled by the original model.
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