10 citations · 33 across the 16 of their papers we have counts for
4 papers · 1 filter
Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
Mariia Drozdova, Vitaliy Kinakh, Yury Belousov +2
In this paper, we present a semi-supervised fine-tuning approach designed to improve the performance of pre-trained foundation models on downstream tasks with limited labeled data.…
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
Vitaliy Kinakh, Mariia Drozdova, Slava Voloshynovskiy
We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR). The MV-MR is based on the maximization of…
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant +2
Conditional generation is a subclass of generative problems where the output of the generation is conditioned by the attribute information. In this paper, we present a stochastic c…
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images
Mariia Drozdova, Anton Broilovskiy, Andrey Ustyuzhanin +1
Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad…