The seminar of OBELIX team is currently held on thursdays 11:30 am, every two weeks, at the IRISA lab, Tohannic campus (bat. ENSIBS). Usually, the presentation lasts 30 min and is followed by a discussion with the team.
The seminar is coordinated by Yann CABANES: Please contact me for any information or if you want to present your work to our team.
Previous seminars
2019 / 2019-20 / 2020-21 / 2021-22 / 2022-23 / 2023-24 / 2024-25
Upcoming seminars (2024-25)
- Date: Thursday, February 6th at 11:30 a.m.
- Room: A102, ENSIBS Vannes
- Speaker: Ambroise Odonnat
- Title: MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation under Distribution Shifts
- Abstract: Leveraging the model’s outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples without requiring access to the corresponding ground-truth labels. Despite their ease of implementation and computational efficiency, current logit-based methods are vulnerable to overconfidence issues, leading to prediction bias, especially under the natural shift. In this work, we first study the relationship between logits and generalization performance from the view of low-density separation assumption. Our findings motivate our proposed method MANO that (1) applies a data-dependent normalization on the logits to reduce prediction bias, and (2) takes the Lp norm of the matrix of normalized logits as the estimation score. Our theoretical analysis highlights the connection between the provided score and the model’s uncertainty. We conduct an extensive empirical study on common unsupervised accuracy estimation benchmarks and demonstrate that MANO achieves state-of-the-art performance across various architectures in the presence of synthetic, natural, or subpopulation shifts.
- Date: Thursday, February 27th at 11:30 a.m.
- Room: To be defined
- Speaker: Antoine Bralet
- Title: Deep learning for multimodal detection of sudden and slow moving slope instabilities on bitemporal remote sensing images
- Abstract: The aim of this work is to develop artificial intelligence algorithms for the detection of sudden and slow slope instabilities from synthetic aperture radar (SAR) and optical satellite images. The first contributions led to the creation of a new neural network architecture, SARDINet, enabling SAR images to be translated into optical images, so that comparable images can be obtained independently of weather conditions. Several architectural modifications improved the relevance and contrast of the translated images. These translations were then conditioned using new cost functions to guide the translator in delivering images that can be used a posteriori for classification or change detection tasks. These translations are then applied to the detection of sudden landslides in Haiti in 2021. Finally, the detection of slow instabilities was the subject of the creation of a new dataset, ISSLIDE. The dataset is based on a new manual routine of annotation on SAR interferograms and exploited by standard segmentation neural networks. It is also used to develop a new segmentation strategy named ECSPLAIN. The latter relies on the explainability of a classifier network during the training phase to generate segmentation maps and force the classifier to be explicable. Thus, this work provides new artificial intelligence methods applied to satellite images, while meeting the need for understanding in the geosciences.
