Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Sevriugov, Egor"'
Autor:
Sevriugov, Egor, Oseledets, Ivan
Non-autoregressive language models are emerging as effective alternatives to autoregressive models in the field of natural language processing, facilitating simultaneous token generation. This study introduces a novel flow matching approach that empl
Externí odkaz:
http://arxiv.org/abs/2411.16821
This paper proposes a novel method, Explicit Flow Matching (ExFM), for training and analyzing flow-based generative models. ExFM leverages a theoretically grounded loss function, ExFM loss (a tractable form of Flow Matching (FM) loss), to demonstrabl
Externí odkaz:
http://arxiv.org/abs/2402.03232
Autor:
Sevriugov, Egor, Oseledets, Ivan
Evaluation metrics are essential for assessing the performance of generative models in image synthesis. However, existing metrics often involve high memory and time consumption as they compute the distance between generated samples and real data poin
Externí odkaz:
http://arxiv.org/abs/2309.00107
Autor:
Sevriugov, Egor, Oseledets, Ivan
Recent advancements in real image editing have been attributed to the exploration of Generative Adversarial Networks (GANs) latent space. However, the main challenge of this procedure is GAN inversion, which aims to map the image to the latent space
Externí odkaz:
http://arxiv.org/abs/2308.16510
Autor:
Kornilova, Anatasiia, Salnikov, Mikhail, Novitskaya, Olga, Begicheva, Maria, Sevriugov, Egor, Shcherbakov, Kirill, Pronina, Valeriya, Dylov, Dmitry V.
Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images. Although some paired image translation and super-resolution approache
Externí odkaz:
http://arxiv.org/abs/2007.13701