Doctoral colloquium - Lukáš Gajdošek (13.11.2023)

Monday 13.11.2023 at 13:10, Lecture room I/9

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Lukáš Gajdošech:
Data Availability from Industrial Hardware in the Era of Consumer-grade Sensor Abundance

Abstract:
Algorithms based on deep learning have increasingly become an integral part of our daily lives. Essential ingredient of these methods, which is often taken for granted, is the availability of large-scale datasets with real-world complexity coverage. In the context of vision tasks, these usually consist of images available online. These samples are often produced by the general public without the prior goal of dataset creation. This is in sharp contrast with the situation in the industry. Tasks like machine part localization by industry-grade robots are still built upon traditional deterministic algorithms. While this may be surprising at the first sight, it makes perfect sense from the perspectives of data availability, reproducibility, robustness and trustworthiness. Not only the factory scenes are vastly different, but also the sensors employed such as 3D cameras, yield data in different format and fidelity than their consumer-grade counterparts. Suddenly, gathering of datasets for deep learning requires unrealistic amounts of time and manual labor in this setting. One solution discussed in this presentation will be the usage of so-called digital twins, resulting in synthetic training data. Challenges of domain gap will be consulted in the context of authors current research on surface-normal prediction of depth maps with missing samples. Furthermore, an analysis of two recently published works of the author from this data importance perspective will follow. Lastly, problems of static datasets and an ever-changing world affecting these solutions will be touched upon with modern approaches such as lifelong and multi-task learning.

 

 

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