Before revision:   To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty . Simulation experiments show that spectral decoupling allows training neural networks on datasets with strong spurious correlations.
After revision:  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty , thus having no added computational costs. We show that spectral decoupling allows training neural networks on datasets with strong spurious correlations.