activity
20182025
most citedAdversarial Augmentation for Enhancing Classification of Mammography Images

3 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SD2025

Live Music Models

Lyria Team, Antoine Caillon, Brian McWilliams +33

We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release M…

cs.LG2020

Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction

Daria Soboleva, Ondrej Skopek, Márius Šajgalík +8

We present a novel multi-modal unspoken punctuation prediction system for the English language which combines acoustic and text features. We demonstrate for the first time, that by…

cs.LG2019

Mixed-curvature Variational Autoencoders

Ondrej Skopek, Octavian-Eugen Ganea, Gary Bécigneul

Euclidean geometry has historically been the typical "workhorse" for machine learning applications due to its power and simplicity. However, it has recently been shown that geometr…

cs.CV20193 cited

Adversarial Augmentation for Enhancing Classification of Mammography Images

Lukas Jendele, Ondrej Skopek, Anton S. Becker +1

Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On t…

cs.CV2018

Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks

Anton S. Becker, Lukas Jendele, Ondrej Skopek +4

To train a cycle-consistent generative adversarial network (CycleGAN) on mammographic data to inject or remove features of malignancy, and to determine whether t…