MAP/INF630 (3 ECTS): Students will work half a day a week on a transverse project, ie. a case study corresponding to a challenging question either raised by an industrial partner or by a researcher in the domain spanned by the graduate degree.

Presentation of transverse projects will occur on 25th September at 2pm in Alan Turing building (on Polytechnique campus) in room Sophie Germain.

Enedis contact Frédéric Boutaud frederic.boutaud@enedis.fr

• Dataposte: Détection automatique du type de matériel en place à partir de photos, pour l'aide à l'intervention lors des missions de maintenance du réseau.
• Predictive maintenance: Problèmes de maintenance prédictive, consistant à anticiper des pannes à venir sur le réseau électrique,
• Customer relationship: Création de chatbot conviviaux pour répondre aux demandes des utilisateurs

Google contact Damien Henry damienhenry@google.com, project Google Arts and Culture.

• Automatic detection of Art Style in paintings: Il existe de nombreuses bases de données non structurée, pour lesquels il pourrait être interessant de detecter automatiquement des méta donnée, en particulier le mouvement artistique, l'auteur, etc… (voir ici, et là).
• Image generation: La generation d'image grâce au ML est en plein essor avec de nombreuses applications possibles. La technique classique est basée sur des Generative Adversarial Network. Une technique plus récente et prometteuse est basée sur les Normalizing Flow. (GAN et GLOW). L'objectif de ce projet est de comparer plusieurs approches pour générer des images de visage à partir d'une base d'images d'apprentissage. Dans les deux cas, des données peuvent être trouvée ici.

Idemia contact Stéphane Gentric stephane.gentric@idemia.com

• Semi-supervised learning for a localization task. (possible continuation in an internship, and eventually a CIFRE PhD) Using a DCNN (Deep Convolution neural Network), we want to learn the absolute position, scale and rotation of an object in an image. Standard methods rely on annotated data and are limited by the precision of those annotations. We want to study the feasibility and performance of a learning process without any annotations, using only the fact that when applying a given similarity to the image, the expected changes in position, scale and rotation are known. We will start with a toy problem and hopefully move on to real objects and more complex scenes.
• Building an image-based algorithm selector for face recognition based on speed and performance of candidate algorithms (possible continuation in an internship). The increasingly ubiquitous presence of biometric solutions and face recognition in particular in everyday life requires Idemia to adapt its solutions for practical requirements, may they be memory space, speed or performances. Idemia has developed several solutions, but where global decisions can be made, they are far less efficient then tailoring such decisions to the complexity of each image, which allows for the best compromise between constraints such as speed and performances. We would like to build a DCNN (Deep Convolution neural Network) selector of the best suited solution to each input image. For this purpose, we will lend a coding/matching software suite capable of generating different options.

Ynsect (start-up; not official partners yet) contact Arturo Escaroz Cetina arturo.escarozcetina@ynsect.com

• Conduite d’élevage 4.0 : Automated Insects Physiological Data Retrival from Insect Population pictures. @Ynsect (www.ynsect.com), our insects are raised into trays of various size : Various pictures of them are taken regularly to perform quality control operations. We would like to enhance our data collection methods to get significant improvement on our insect population modeling tools. The objective of this project is to convert pictures into already know data of interest (visual computing) & to make data driven R&D into the discoveries of any observable Behavior patterns through pictures (AI). Already known data of interest can be picked in : insect Size, stage, density, color, number of rings, defects, behavior, Amount of feed / top layer description, population distribution pattern; etc. The project might include: data collection methods’ revision; Image characterization and classification; Pattern recognition & Predictive tools’; Any other methods tools that could be of interest and that we don’t know of yet !