1 code implementation • 15 Feb 2024 • Mariia Drozdova, Vitaliy Kinakh, Omkar Bait, Olga Taran, Erica Lastufka, Miroslava Dessauges-Zavadsky, Taras Holotyak, Daniel Schaerer, Slava Voloshynovskiy
Current techniques, such as CLEAN and PyBDSF, often fail to detect faint sources, highlighting the need for more accurate methods.
no code implementations • 11 Nov 2023 • Guillaume Quétant, Yury Belousov, Vitaliy Kinakh, Slava Voloshynovskiy
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods.
no code implementations • 28 Sep 2023 • Yury Belousov, Olga Taran, Vitaliy Kinakh, Slava Voloshynovskiy
Copy detection patterns (CDP) present an efficient technique for product protection against counterfeiting.
1 code implementation • 21 Mar 2023 • 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).
Ranked #2 on Unsupervised Image Classification on STL-10
no code implementations • 20 Dec 2021 • Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh, Tobias Golling, Slava Voloshynovskiy
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model.
1 code implementation • 17 Dec 2021 • Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy
The InfoSCC-GAN architecture is based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an attribute classifier and an EigenGAN generator.
1 code implementation • 31 Aug 2021 • Vitaliy Kinakh, Olga Taran, Svyatoslav Voloshynovskiy
In this paper, we consider a problem of self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data, which demonstrates the state-of-the-art performance in classification task.
Ranked #1 on Unsupervised Image Classification on CIFAR-20