Image recognition is used in a lot of application nowadays, it is used for example in sign recognition in autonomous cars. Neural networks are well-suited for this task and perform well in them, but the networks themselves are not well understood. Sometimes the model learns something else than intended, for example the forest in the background and not the road sign. This behavior is called spurious correlations and this paper investigates if new methods can reduce this behavior in neural networks. The new method that is used in this paper is called similarity loss. The results do indicate that this method does not significantly reduce spurious correlations.