ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2020-23-6-1142-1154ellibs-253Research ArticleСтатьиУвеличение робастности нейронных сетей за счет генерации векторных представлений, инвариантных к атрибутамOf Neural Network Model Robustness Through Generating Invariant to Attributes EmbeddingsГазизовМ. Р.GazizovM. R.gazizovmarat@gmail.comГригорянК. А.GrigorianK. A.karigri@yandex.ru20202812202023611421154Copyright © Газизов М.Р., Григорян К.А., 20202020Газизов М.Р., Григорян К.А.Gazizov M.R., Grigorian K.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/253

Робастность модели к незначительным отклонениям в распределении исходных данных является важным критерием во многих задачах. Нейронные сети могут показывать высокую точность (accuracy) на обучающей выборке, но при этом качество на тестовой выборке может сильно падать из-за разного распределения данных, причем ситуация только усугубляется на уровне подгрупп внутри каждой категории. В данной статье мы показываем, как робастность модели на уровне подгрупп может быть значительно улучшена с помощью подхода, основанного на доменной адаптации векторных представлений. Мы обнаружили, что применение состязательного подхода к ограничению векторных представлений дает существенный прирост метрики точности (accuracy) в сложной подгруппе по сравнению с предыдущими моделями. Метод протестирован на двух независимых наборах данных, точность в сложной подгруппе на наборе данных Waterbirds составляет 90.3 {y : waterbirds;a : landbackground}, а на наборе данных CelebA – 92.22 {y : blondhair;a : male}.

Model robustness to minor deviations in the distribution of input data is an important criterion in many tasks. Neural networks show high accuracy on training samples, but the quality on test samples can be dropped dramatically due to different data distributions, a situation that is exacerbated at the subgroup level within each category. In this article we show how the robustness of the model at the subgroup level can be significantly improved with the help of the domain adaptation approach to image embeddings. We have found that application of a competitive approach to embeddings limitation gives a significant increase of accuracy metrics in a complex subgroup in comparison with the previous models. The method was tested on two independent datasets, the accuracy in a complex subgroup on the Waterbirds dataset is 90.3 {y : waterbirds;a : landbackground}, on the CelebA dataset is 92.22 {y : blondhair;a : male}.

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The authors declare that there are no conflicts of interest present.