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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development and research of methods for local interpretation of the Siamese neural network
Creators: name: ÐÐ°ÑÐ¸Ð¼Ð¾Ð², Ð­ÑÐ½ÐµÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ¸ÑÑÐµÐ¼Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
Ð¾Ð±ÑÑÑÐ½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÑÐ¸Ð°Ð¼ÑÐºÐ°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ¶Ð°ÑÐ¾Ðµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð´Ð°Ð½Ð½ÑÑ
Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑ
Ð²Ð½ÐµÑÐµÐ½Ð¸Ðµ Ð²Ð¾Ð·Ð¼ÑÑÐµÐ½Ð¸Ð¹
interpreted model
explanatory intelligence
siamese neural network
compressed data representation
autoencoder
perturbation

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description: Theme of final qualification work: âDevelopment and research of methods for local interpretation of the Siamese neural networkâ. This work is devoted to the study of the developed interpretation method based on the use of an auto encoder and a perturbation method. Tasks that were solved during the study: 1. The study of existing methods for interpreting the operation of neural networks 2. Development of an auto encoder with a special loss function. 3. The study of the developed method on a synthetic data set 4. Study of the method on the MNIST dataset The paper proposes a method for explaining the result of the Siamese neural network, which acts as a âblack boxâ, provided that for each subnet an output vector of compressed data is available. The main problem is that the perturbation method cannot be used directly on input instances, since only their semantic similarity or difference is known. Therefore, it is proposed to use an autocoder with a special loss function, which is trained in such a way that the result of its hidden layer is close to the output of the Siamese neural network, and the result on the output layer is close to the input data. The decoding part is used to restore the original instances from the perturbed output vectors of the Siamese network. Important features of the explained specimens are determined by averaging the corresponding changes in the reconstructed specimens. Synthetic data and the MNIST data set are used to represent the health of the method.