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CLIPTraVeLGAN for Semantically Robust Unpaired Image Translation
(2023) Bodyanskiy, Y.; Ryabova, N.; Lavrynenko, R.
In this paper a novel approach for semantically robust unpaired image translation is presented. CLIPTraVeLGAN replaces the Siamese network in TraVeLGAN with a contrastively pretrained language-image model (CLIP) with frozen weights. This approach significantly simplifies the model selection and training process of TraVeLGAN, making it more robust and easier to use.
Named Entity Recognition Problem for Long Entities in English Texts
(2021) Shatalov, O.; Ryabova, N.
This paper is related to the problem of natural language processing (NLP), namely the named entity recognition (NER). This paper reveals the features of named entities recognition in English texts using deep learning (DL). The peculiarity of the study was the rather long length of the presented named entities: many of them could include a rather large number of words. The main problem was the amount of text that had to be recognized as a single entity. The results of the research are described here show the effectiveness of using deep neural network architectures for the task of recognizing long named entities in texts in English.