L2 regularisation is often used in training deep neural networks to reduce overfitting to noise by promoting learnt weights to have a lower l2-norm. We see that while we do not explicitly regularise the models using l2-norm, base augmentations and MixUp lead to substantially lower norms of the learnt convolutional kernels on models trained for both cardiac cine MR (Fig.~\ref{fig:reg_effect_acdc}) and prostate bpMRI (Fig.~\ref{fig:reg_effect_p158}).

\input{figures/conv_norms/acdc}

\input{figures/conv_norms/p158}
