title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÑÐ¸Ðº ÑÐ°ÑÐ¿ÑÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð»Ð°ÑÐµÐ½ÑÐ½Ð¾Ð³Ð¾ ÑÐ»Ð¾Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Investigation on applicability of disentanglement representation score factors
Creators: name: ÐÐ°Ð²ÑÐ¸ÑÐµÐ½ÐºÐ¾, ÐÐ»ÑÐ³Ð°
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: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð±ÐµÐ· ÑÑÐ¸ÑÐµÐ»Ñ
Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸Ðº
Ð»Ð°ÑÐµÐ½ÑÐ½Ð¾Ðµ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ð¾
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿ÑÑÐ°Ð½Ð½Ð¾Ðµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ
machine learning
unsupervised learning
disentanglement representation
autoencoder
deep learning
latent space

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description: The related work consists of investigating the autoencoder training process dependency on artificial factors representing and describing the input data. When the latent space totally not correlating itself and all the factors are mapped to the latent space variables (for instance eye color, face shape, etc.) we call such realization the disentangled representation. The disentangling enable to be used as following: 1. Increase the accuracy of trained model 2. To be used for data compression 3. Being able to generate new data relying the existing one There is a hypothesis: the smaller dimensional factor space exists and factors in this space are better describing the data and factors are interpretable Hence the disentangled representation still does not have final determination it is because of no any resolution on disentanglement score set in the scientific society. In this paper the consistency of three existing disentanglement indicators (MIG, FactorVAE, IRS) are declined in a proof of several models such as: PCA, Downsampling, VAE. The combination of metrics and practices proposed in this work.