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: December, 16 (WEDNESDAY) // joint seminar with Copernicus Master in Digital Earth
  • Time: 11:30
  • Room : visio
  • Speaker: Ronan Fablet (LabSTICC/TOMS, IMT Atlantique, Brest)
  • Title: Bridging physics and deep learning fo geophysical dynamics: application to ocean monitoring and surveillance
  • Abstract: Whereas model-driven approaches represent the state-of-the-art for the analysis, simulation and reconstruction of physical systems, learning-based and data-driven frameworks become relevant schemes for a large number of application domains, including for the study of phenomena governed by physical laws. They offer new means to take advantage of the potential of observation and/or simulation big data. In this context, making the most of model-driven and data-driven paradigms naturally arises as a key challenge.
    In this talk, we will discuss these research avenues and give some illustrations on ocean monitoring applications (e.g., reconstruction of sea surface tracers, maritime traffic surveillance). We will specifically address how neural networks can provide novel means for the data-driven identification of representations of dynamical systems, which are imperfectly observed (e.g., noisy data, partial observation, irregular sampling..).
    Selected references: (full paper available on )
    R. Fablet et al. Joint learning of variational representations and solvers for inverse problems with partially-observed data. Arxiv, 2020.
    R. Lguensat et al. The Analog Data Assimilation. MWR 2017.
    V.D. Nguyen, R. Vadaine, G. Hajduch, R. Garello, R. Fablet. Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams. IEEE DSAA, 2018.V.D.
    Nguyen, S. Ouala, L. Drumetz, R. Fablet. EM-like Learning Chaotic Dynamics from Noisy and Partial Observations. arXiv preprint arXiv:1903.10335. 2019.
    S. Ouala, V.D. Nguyen, (…), R. Fablet. Learning Latent Dynamics for Partially Observed Systems. Chaos, 2020.
    F. Rousseau, L. Drumetz, R. Fablet. Residual Networks as Geodesic Flows of Diffeomorphisms. JMIV, 2019.

  • Date: December, 16 (WEDNESDAY)
  • Time: 14h00
  • Room : Visio here
  • Speaker: Caglayan Tuna (PhD student, OBELIX) – PhD Defense
  • Title: Morphological Hierarchies for Satellite Image Time Series
  • Abstract:  Although morphological hierarchies are today a well-established framework for single frame image processing, their extension to time-related data remains largely unexplored. This thesis aims to tackle the analysis of satellite image time series with tree-based representations. To do so, we distinguish between three kinds of models, namely spatial, temporal and spatial-temporal hierarchies. For each model, we propose a streaming algorithm to update the tree when new images are appended to the series. Besides, we analyze the structural properties of the different tree building strategies, thus requiring some projection methods for the spatio-temporal tree in order to obtain comparable structures. Then, trees are compared according to their node distribution, filtering capability and cost, leading to a superiority of the spatio-temporal tree (a.k.a. space-time tree). Hence, we review spatio-temporal attributes, including some new ones, that can been extracted from the space-time tree in order to compute some multiscale features at the pixel or image level. These attributes are finally involved in tools such as filtering and pattern spectrum for various remote sensing based applications.

  • Date: December, 4
  • Time: 9h30
  • Room : RENNES 2 Visio
  • Speaker: Romain Tavenard (LETG Rennes & OBELIX, IRISA) – HDR Defense
  • Title:  Apprentissage statistique et séries temporelles
  • Abstract: A Jupyter Book version of the thesis is available online :

  • Date: December, 3 (THURSDAY) // joint seminar with Copernicus Master in Digital Earth
  • Time: 11:30
  • Room : VISIO
  • Speaker: Damien Arvor (CR CNRS, LETG, Rennes2)
  • Title: Monitoring agricultural dynamics in the southern brazilian Amazon with remote sensing data
  • Abstract: Les dynamiques agricoles dans le sud de l’Amazonie brésilienne impactent fortement les écosystèmes naturels. Bien que la déforestation ait largement baissé depuis 2004, les processus d’expansion, d’intensification et de diversification agricole se poursuivent et soulèvent de nouvelles interrogations, notamment dans un contexte de changement climatique. L’imagerie satellitaire et ses produits dérivés (e.g. cartes d’occupation du sol) apportent des informations précieuses pour apporter des éléments de réponse à ces questions de recherche. Par exemple, les séries temporelles d’indice de végétation sont utilisées pour cartographier les pratiques agricoles alors que les estimations de précipitations permettent de spatialiser les premières évidences du changement climatique. Toutefois, les méthodes de traitement d’images satellitaires favorisées aujourd’hui (e.g. machine learning) présentent certaines limitations qui peuvent gêner l’interprétation de l’information spatiale par des utilisateurs aux objectifs divers et variés (géographes, écologues, climatologues, etc). Il convient donc de discuter de l’intérêt de méthodes « kwowledge-driven » pour l’interprétation des images satellitaires, en soulignant notamment l’apport des ontologies.

  • Date: November, 5
  • Time: 9h30
  • Room : visio (link : if you missed the defense and you want to see it )
  • Speaker: Titouan Vayer (OBELIX, IRISA Vannes) – PhD Defense
  • Title: A contribution to Optimal Transport on incomparable spaces
  • Abstract: Optimal Transport is a theory that allows to define geometrical notions of distance between probability distributions and to find correspondences, relationships, between sets of points. Many machine learning applications are derived from this theory, at the frontier between mathematics and optimization. This thesis proposes to study the complex scenario in which the different data belong to \emph{incomparable} spaces. In particular we address the following questions: how to define and apply the optimal transport between graphs, between structured data? How can it be adapted when the data are varied and not embedded in the same metric space? This thesis proposes a set of Optimal Transport tools for these different cases. An important part is notably devoted to the study of the Gromov-Wasserstein distance whose properties allow to define interesting transport problems on incomparable spaces. More broadly, we analyze the mathematical properties of the various proposed tools, we establish algorithmic solutions to compute them and we study their applicability in numerous machine learning \emph{scenarii} which cover, in particular, classification, simplification, partitioning of structured data, as well as heterogeneous domain adaptation.

  • Date: October, 23 (FRIDAY)
  • Time: 9h30-17h00
  • Room :  A105-107, ENSIBS, Vannes
  • Title: Seminaire au vert! (Annual team seminar)
  • Program here

  • 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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