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 ISNet) to eight state-of-the-art DNNs, quantitatively demonstrating its superior robustness to background bias. Mixed datasets are common for COVID-19 and tuberculosis classification with chest X-rays, fostering background bias. By focusing on the lungs, the ISNet reduced shortcut learning. Thus, its generalization performance on external (out-of-distribution) test databases significantly surpassed all implemented benchmark models.

Deep learning-based classification of anti-personnel mines and sub-gram metal content in mineralized soil (DL-MMD)

“…The information exists, but it needs to be pre-processed before feeding the signal to the model. One approach could be to use focused AI models, similar to those shown in Ref 23 , that inject synthetic bias into the signal to generalize the model in our case at different depths. Another approach can be to localize the area with different decay rates, similar to the one shown in Ref 24 for 2D image application.…”