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seminaire@lrde.epita.fr

April 2019

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Mercredi 10 avril 2019: Guillaume Charpiat (TAU-team, INRIA Saclay / LRI - Université Paris-Sud)
by Edwin Carlinet 02 Apr '19

02 Apr '19

Chers collègues, La prochaine session du séminaire Performance et Généricité du LRDE (Laboratoire de Recherche et Développement de l'EPITA) aura lieu le Mercredi 10 avril 2019 (11h -- 12h), Amphi 4. Vous trouverez sur le site du séminaire [1] les prochaines séances, les résumés, captations vidéos et planches des exposés précédents [2], le détail de cette séance [3] ainsi que le plan d'accès [4]. [1] http://seminaire.lrde.epita.fr [2] http://seminaire.lrde.epita.fr/Archives [3] http://seminaire.lrde.epita.fr/2019-04-10 [4] http://www.lrde.epita.fr/wiki/Contact Au programme du Mercredi 10 avril 2019 : * 11h -- 12h: Deep Learning for Satellite Imagery: Semantic Segmentation, Non-Rigid Alignment, and Self-Denoising -- Guillaume Charpiat (TAU-team, INRIA Saclay / LRI - Université Paris-Sud) https://www.lri.fr/~gcharpia/ Neural networks have been producing impressive results in computer vision these last years, in image classification or segmentation in particular. To be transferred to remote sensing, this tool needs adaptation to its specifics: large images, many small objects per image, keeping high-resolution output, unreliable ground truth (usually mis-registered). We will review the work done in our group for remote sensing semantic segmentation, explaining the evolution of our neural net architecture design to face these challenges, and finally training a network to register binary cadaster maps to RGB images while detecting new buildings if any, in a multi-scale approach. We will show in particular that it is possible to train on noisy datasets, and to make predictions at an accuracy much better than the variance of the original noise. To explain this phenomenon, we build theoretical tools to express input similarity from the neural network point of view, and use them to quantify data redundancy and associated expected denoising effects. If time permits, we might also present work on hurricane track forecast from reanalysis data (2-3D coverage of the Earth's surface with temperature/pressure/etc. fields) using deep learning. -- After a PhD thesis at ENS on shape statistics for image segmentation, and a year in Bernhard Schölkopf's team at MPI Tübingen on kernel methods for medical imaging, Guillaume Charpiat joined INRIA Sophia-Antipolis to work on computer vision, and later INRIA Saclay to work on machine learning. Lately, he has been focusing on deep learning, with in particular remote sensing imagery as an application field. L'entrée du séminaire est libre. Merci de bien vouloir diffuser cette information le plus largement possible. N'hésitez pas à nous faire parvenir vos suggestions d'orateurs. -- Edwin Carlinet Laboratoire R&D de l'EPITA (LRDE)

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