Iterative neural networks (INNs) currently define the state-of-the-art for image reconstruction methods. With these methods, the obtained regularizers are not only optimally adapted to the employed physical model but also tailored to the reconstruction method the network implicitly defines. However, comparing the performance of different INNs-based methods is often challenging because of the black-box character of neural networks. In this work we construct an example which highlights the importance of keeping the number of trainable parameters approximately fixed when comparing INNs-based methods. If this aspect is not taken into consideration, wrong conclusions could be drawn.
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