The Machine Learning Lab

Our R&D projects and publications

The Machine Learning Lab (MLLab) was founded in 2018. It is a part of the Faculty of Applied Sciences, Ukrainian Catholic University in Lviv. Our research has several branches of focus and we are constantly expanding and looking for new challenges. Сurrently, we are working on: machine learning and deep learning algorithms applied to the supervised and weakly-supervised visual recognition problems; robust single view geometry estimation; reinforcement learning for optimization and control problems. Our research center has well-established connections with industry and academia.  


Aug 7, 2020 The work of Yaroslava Lochman, Oles Dobosevych, Rostyslav Hryniv, and James Pritts got accepted for Oral Presentation at WACV 2021. Paper and code coming soon.
Jun 14, 2020 Mariia Dobko, Ostap Viniavskyi, and Oles Dobosevych took the 3rd place at CVPR 2020 LID Challenge. See the paper NoPeopleAllowed: The Three-Step Approach to Weakly Supervised Semantic Segmentation and recorded presentation.
May 4, 2020 The work of James Pritts, Zuzana Kukelova, Viktor Larsson, Yaroslava Lochman, and Ondrej Chum was published in TPAMI 2020. See the paper Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations and code.


Weakly supervised semantic segmentation

To create models which can effectively segment images, the immense datasets of labeled data are required. It is often very time consuming. When labeling medical data an expert has to spend a lot of time and use his expertise which is very expensive.

In order to decrease the resources spent on labeling while preserving the quality of the models, we develop a weakly-supervised approach for segmentation of different types of images. We test our method and show results on several distant datasets, including regular real-world objects (such as cars, planes, trees etc.), and on medical data (chest X-rays). We develop a solution which is capable to segment objects with a limited supervision.

ModelicaGym Toolbox for Applying Reinforcement Learning to Modelica Models

ModelicaGym toolbox was developed to employ Reinforcement Learning (RL) for solving optimization and control tasks in Modelica models. The developed tool allows connecting models using Functional Mock-up Interface (FMI) to OpenAI Gym toolkit in order to exploit Modelica equation-based modeling and co-simulation together with RL algorithms as a functionality of the tools correspondingly. Thus, ModelicaGym facilitates fast and convenient development of RL algorithms and their comparison when solving optimal control problem for Modelica dynamic models. The toolbox functionality validation is performed on Cart-Pole balancing problem.

Authors: Oleh Lukianykhin, Tetiana Bogodorova

Robust Methods for Single View Geometry Estimation

The project focuses on robust methods for camera auto-calibration, estimating scene plane rectification and symmetry detection. These methods are useful for important vision tasks like visual localization and 3D reconstruction.

- James Pritts (Facebook Reality Labs, Supervisor)
- Yaroslava Lochman (UCU)
- Kostiantyn Liepieshov (UCU)
- Oles Dobosevych (UCU)
- Rostyslav Hryniv (UCU)

Face Recognition system

We developed a loyalty system for retail based on the face recognition. A user has to register via mobile phone and take one photo. After that based on the angularly discriminative features we build engine for face recognition. Now when the registered client comes to the shop they will be recognised and the shop assistant will immediately get all the information about the client's tastes so they can recommend better items.

Authors: Oles Dobosevych, Anton Tarasov, Matt Kovtun, Oleh Smolkin, Mykhailo Ivankiv

Ukrainian style MNIST

Modified National Institute of Standards and Technology dataset (MNIST dataset) of handwritten digits is the most known dataset that is widely used as a benchmark for validating various ideas in Machine Learning. We present a newly created dataset of 32 handwritten Ukrainian letters, which is divided into 72 different style subclasses, with 2000 examples in each class. We also suggest a recognition model for these symbols and explain why approaches working well for MNIST dataset do not succeed in our case. Finally, we discuss several real-world applications of our model that can help to save paper, time and money.

Authors: Oles Dobosevych, Matvii Kovtun

Menu generator

We developed a system that allows restaurant owners to customise menus for their restaurants in a few clicks and FFS owners to collect and analyse data about dishes that restaurants prefer to choose.

Authors: Kostiantyn Liepieshov, Vladyslav Ursul

Low cost system to record punch press parameters

We developed an IoT solution to automatically count the number of overall press strikes as well as granulate this data to individual molds with minimum operator intervention.

Authors: Dzvenymyra Yarish, Yurii Laba, Yurii Stasinchuk, Oleksandr Pryhoda, Bogdan Petryshak


J. Pritts, Z. Kukelova, V. Larsson, Y. Lochman, O. Chum. Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020


J. Pritts, Z. Kukelova, V. Larsson, Y. Lochman, O. Chum. Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales. International Journal of Computer Vision (IJCV) 2020


O. Kupyn, D. Pranchuk. Fast and Efficient Model for Real-Time Tiger Detection In The Wild. arXiv preprint, 2019


O. Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang. DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. arXiv preprint, accepted for ICCV 2019


Irynei Baran, O. Kupyn, A. Kravchenko. Safe Augmentation: Learning Task-Specific Transformations from Data. arXiv preprint, accepted for WACV 2020


O. Lukianykhin, T. Bogodorova. ModelicaGym: Applying Reinforcement Learning to Modelica Models. arXiv preprint, accepted for publication in EOOLT 2019


O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, J. Matas. DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks, IEEE CVPR, Salt Lake City, USA, June 2018
Publication, Poster


M. Mykhailych. Application of Generative Neural Models for Style Transfer Learning in Fashion, Master Thesis, 2018


A. Stehnii. Generation of code from text description with syntactic parsing and Tree2Tree model, Master Thesis, 2018