  A new method for explaining the Siamese neural network is proposed. It uses
the following main ideas. First, the explained feature vector is compared with
the prototype of the corresponding class computed at the embedding level (the
Siamese neural network output). The important features at this level are
determined as features which are close to the same features of the prototype.
Second, an autoencoder is trained in a special way in order to take into
account the embedding level of the Si-amese network, and its decoder part is
used for reconstructing input data with the corresponding changes. Numerical
experiments with the well-known dataset MNIST illustrate the propose method.
