Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model

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.

Astronomy Denoising +2

MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation

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).

Clustering Contrastive Learning +3

Turbo-Sim: a generalised generative model with a physical latent space

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.

Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN

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.

Attribute Generative Adversarial Network +1

A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images

no code implementations • 8 Jul 2020 • Mariia Drozdova, Anton Broilovskiy, Andrey Ustyuzhanin, Denys Malyshev

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 point spread function of modern space-based instruments.