no code implementations • 13 May 2020 • Onur Tasar, Alain Giros, Yuliya Tarabalka, Pierre Alliez, Sébastien Clerc
We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images.
no code implementations • 14 Apr 2020 • Onur Tasar, Yuliya Tarabalka, Alain Giros, Pierre Alliez, Sébastien Clerc
However, these methods have limited practical real world applications, since usually one has multiple source domains with different data distributions.
no code implementations • 14 Feb 2020 • Onur Tasar, S. L. Happy, Yuliya Tarabalka, Pierre Alliez
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data.
1 code implementation • 30 Jul 2019 • Onur Tasar, S. L. Happy, Yuliya Tarabalka, Pierre Alliez
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations.
no code implementations • 29 Oct 2018 • Onur Tasar, Yuliya Tarabalka, Pierre Alliez
The key points of the proposed approach are adapting the network to learn new as well as old classes on the new training data, and allowing it to remember the previously learned information for the old classes.
no code implementations • 10 Oct 2015 • Baris Gecer, Ozge Yalcinkaya, Onur Tasar, Selim Aksoy
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects.