Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization.

ABSTRACT: Features in images' backgrounds can spuriously correlate with the images' classes, representing background bias. They can influence the classifier's decisions, causing shortcut learning (Clever Hans effect). The phenomenon generates deep neural networks (DNNs) that perform well on standard evaluation datasets but generalize poorly to real-world data. Layer-wise Relevance Propagation (LRP) explains DNNs' decisions. Here, we show that the optimization of LRP heatmaps can minimize the background bias influence on deep classifiers, hindering shortcut learning. By not increasing run-time computational cost, the approach is light and fast. Furthermore, it applies to virtually any classification architecture. After injecting synthetic bias in images' backgrounds, we compared our approach (dubbed

SUBMITTER: Bassi PRAS 

PROVIDER: S-EPMC10767127 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature