Seminars – 2021-2022

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

The seminar is coordinated by Chloé Friguet.

Previous seminars – 2021-2022

  • Date:  november, 25th
  • Time: 14:00
  • Room: IRISA Rennes Salle Métivier (and visio)
  • Speaker: Kilian Fatras (PhD defense)
  • Title: OT and Deep Learning: Learning from one another
  • Abstract: Deep learning (DL) models are artificial neural networks and they have arisen as the current most competitive method to make data-driven decisions. In classification, these networks have a more complex representation of data and thus they make more complex predictions. However, DL’s recent successes are also due to the development of some mathematical fields : this thesis is about studying the different interactions of DL with one of these fields called Optimal Transport (OT).To measure the distance between probability distributions, one can rely on the OT theory. It defines a measure through the minimal displacement cost of a distribution to another. Its strength is to use the space geometry with a given ground cost on the data space. Several DL methods are built upon this theory.This thesis proposes to explore two faces of the interaction between OT and DL. We will first focus on how OT can define meaningful cost functions for neural networks. We will focus on how to define an OT based regularization for learning with noisy labels and how we can use OT to generate misclassified data for a pre-trained classifier. We will then explore what can be learnt about OT from DL applications, with a focus on the minibatch approximation of OT. We will answer what are the gains and downsides of the minibatch formulation both in theory and practice.
    Jury : Marco Cuturi / Christian WolfJury / Laetitia Chapel / Adam M. Oberman / Michèle Sebag

  • Date:  november, 18th
  • Time: 11h30
  • Room: D-001 (bat ENSIBS)
  • Speaker: Joachim Nyborg (PhD candidate, dpt Computer science, Aarhus Univ, Danemark)
  • Title: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
  • Abstract: The recent developments of deep learning models that capture the complex temporal patterns of crop phenology have greatly advanced crop classification of Satellite Image Time Series (SITS). However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions. To address this unsupervised cross-region adaptation setting, existing methods learn domain-invariant features without any target supervision, but not the temporal shift itself. As a consequence, these techniques provide only limited benefits for SITS. In this paper, we propose TimeMatch, a new unsupervised domain adaptation method for SITS that directly accounts for the temporal shift. TimeMatch consists of two components: 1) temporal shift estimation, which estimates the temporal shift of the unlabeled target region with a source-trained model, and 2) TimeMatch learning, which combines temporal shift estimation with semi-supervised learning to adapt a classifier to an unlabeled target region. We also introduce an open-access dataset for cross-region adaptation with SITS from four different regions in Europe. On this dataset, we demonstrate that TimeMatch outperforms all competing methods by 11% in F1-score across five different adaptation scenarios, setting a new state-of-the-art for cross-region adaptation.

  • Date:  october, 14th
  • Time: 11h30
  • Room: D-001 (bat ENSIBS)
  • Speaker :  Hugo Gangloff (Post doc OBELIX)
  • Title: Generalized pairwise Markov models and applications to unsupervised
    image segmentation
  • Abstract: Probabilistic graphical models such as hidden Markov models have found many applications in signal processing. In this presentation, we study on a particular extension of these models, the pairwise Markov  models. We follow an approach which enables us to combine pairwise Markov models with recent architectures from machine learning such as deep neural networks. We focus on the parameter estimation in these hybrid models by an approach based on the Expectation-Maximization algorithm linked with additional constraints for the interpretability of the hidden process to recover. As an illustration, the models will be applied to unsupervised image segmentation, especially when a correlated noise corrupts the image.

  • Date:  September, 24th (FRIDAY)
  • Room Ile Bailleron
  • Title: Seminaire au vert! (Annual team seminar)
  • Program


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