Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Unsupervised Domain Adaptation

Participants : Raoul de Charette, Maximilian Jaritz, Fawzi Nashashibi, Fabio Pizzati.

There is an evident dead end to the paradigm of supervised learning, as it requires costly human labeling of millions of data frames to learn the appearance models of objects. As of today, the databases are recorded in very narrow conditions (e.g. only clear weather, only USA, only daytime). Adjusting to unseen conditions such as snow, hail, nighttime or unseen cities, require supervised algorithms to be retrained. Conversely, as humans we're capable of generalizing prior knowledge to new tasks. During this year, we initiated two works on transfer learning, typically Unsupervised Domain Adaptation (UDA) which is crucial to tackle the lack of annotations in a new domain. We have conducted two parallel projects on UDA: the first one in the scope of Maximilian Jaritz' thesis [27] (submitted), and the second one in the scope of Fabio Pizzati's work on rainy scenarios: