To recover the underlying paths from a given signature representation can not only give confidence in using signature as features in machine learning tasks but also be useful for data augmentation or key-frame extraction. The "Signatory" python package allows the signature transformation to act as a layer in a trainable neural network. Inspired by it, we tried to recover different types of underlying paths from a given signature-based representation. By visualizing the results, we discussed the effects of different hyper-parameters for our signature feature set.