Seminars – 2020-2021

The seminars of OBELIX team are currently held every two weeks at the IRISA lab, Tohannic campus, room D106.

The seminar is coordinated by Chloé Friguet.

Previous seminars – 2020-2021

  • Date: October, 1st
  • Time: 11:30
  • Room : amphi A 101 / DSEG
  • Speaker: Badie Belmouhcine (post doc IFREMER / IRISA-OBELIX)
  • Title: Generative Adversarial Networks for 3D voxelgrid generation
  • Abstract: Generative Adversarial Networks are successful and powerful generative models, that were applied successfully for many tasks such as image and video generation, style transfer, in-painting, domain adaptation, etc. The generation of 3D shapes is particularly challenging, due to the high dimensional input space and the lack of training data.
    During my internship in the CNAM, we studied the use of Generative Adversarial Networks for 3D voxelized object generation and how we can improve their performance by leveraging some properties of 3D voxelized shapes. We first showed that the training of Wasserstein GANs for 3D shape generation can be significantly improved by combining the Gradient Penalty with Spectral Normalization. We also proposed to adapt the model to match the requirement of binary voxel generation, by employing softargmax in the generator. Then, we modified the cost function to enforce the plane symmetry of the generated objects. Finally, since 3D voxelized objects are more characterized by the structure than by the texture, we started to evaluate the use of Self-Attention GANs and Non-local networks in the 3D context, by modeling long range dependencies across different regions of the voxel grid.

  • Date: Septembre, 23rd (WEDNESDAY)
  • Time: 11:00
  • Room : Salle du conseil / bât. Présidence – campus de Tohannic
  • Speaker:  Lucas Drumetz (MCF Traitement du signal, IMT Brest)
  • Title: Learning Dynamical Systems from data, application to multi and hyperspectral time series
  • Abstract: The evergrowing amount of remote sensing data acquired from satellites make it possible to access historical catalogs of image data for multiple applications, e.g. for ocean altimetry or terrestrial optical images. If data assimilation techniques have been extensively used to take advantage of those observations for the former, the time dimension, however, is often neglected in some data processing pipelines for the latter (e.g. classification and unmixing), even when the end-goal is the characterization of the dynamics of the imaged scene. This is mainly due to the fact that the data is partial and noisy. In this talk, I will present a few machine learning techniques and methods allowing to learn dynamical systems from data, mainly using dedicated neural network architectures. I will present some associated methodological challenges (dealing with chaotic systems, stochastic systems, partial data…) and show a few illustrations on toy datasets and a detailed application to simulated and real time-series of satellite multi/hyperspectral data.


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