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PROPOSITION DE POST-DOCTORAT
Référence : PDOC-DTIS-2017-07
(à rappeler dans toute correspondance)
Laboratoire d’accueil à l’ONERA :
Domaine : Traitement de l’Information et Systèmes
Lieu (centre ONERA) : Palaiseau
Département : DTIS
Unité : Perception Sémantique et Raisonnement
Contacts :
Stéphane HERBIN – [email protected] – +33 1 80 38 65 69
Intitulé : Safety of machine learning based algorithms for critical computer vision applications
Keywords:
Machine Learning, Computer Vision, Deep Learning, Safety of algorithms, Statistical
tests, Adversarial networks.
Context :
Machine learning techniques are now ubiquitous in many areas of information and data processing.
Their main justification lies in the level of performance they have achieved when compared to
approaches that rely on explicit and formal models. The recent success of Deep Learning techniques
applied to visual scene understanding is one of the application domains where this evolution has been
clearly established and has reached a performance level that was thought inaccessible few years ago.
It seems unlikely that this trend will backtrack radically at short notice: most of signal and data
analysis approaches will now include somewhere in their processing pipeline one or several
components that have been designed using machine learning techniques.
The recent performance gain makes now possible the practical use of machine learning designed
components in complex systems as exemplified by the recent demonstrations of so-called
"autonomous cars". However, in this kind of application, the computer system ("Autopilot") able to
drive the vehicle is only considered to be an assistant: when the artificial system fails, it is the driver's
responsibility to detect the system abnormal behavior and to ultimately take control of the vehicle.
However, for critical functions such as automatic landing, automated medical diagnosis or "see and
avoid" capability in complex dynamic environments, abnormal behaviors may not be detected
sufficiently early and may contaminate other components of the system, potentially leading to
disastrous consequences.
One question that naturally arises to improve the usability of machine learning based processes is to
provide a way to guarantee and even control a given level of performance. This is a difficult question
for such processes which are effective in their empirical domain of expertise – the learning database –
but are often unable to state why they are so.
Proposed work :
The post-doctoral study will address the question of assessing the good behavior of systems involving
components parametrized by machine learning techniques. The emphasis will be on deep learning
approaches that are now the current state of the art in many signal processing and computer vision
applications.
GEN-F213-2 (GEN-SCI-034)
There will be several possible directions of research to achieve this objective:
• Characterize the system empirical domain of expertise by analyzing the quality of the learning
database. This could lead either to tests able to detect whether the input data can be correctly
processed or to data collecting strategies able to safely and extensively cover the target
domain.
• Define on line tests able to detect potential abnormal behavior. One possibility is to generate
specific outputs able to describe, explain or evaluate the internal behavior and warn of an
expected or abnormal functioning.
• Design efficient stress tests able to detect potential risks when using a given algorithm and
able to qualify it for a given usability domain. This objective could make use of generative
adversarial approaches that have been successfully proposed recently.
It is expected from the candidate to address theoretical issues that will be validated on computer
vision functions, typically object recognition and semantic segmentation.
Expected outputs :
Publication in a high impact factor journal or conference in Computer Vision, Machine Learning or
Artificial Intelligence.
Environment :
The candidate will be involved in two ONERA research projects: DELTA addressing deep learning
techniques applied to aerospace problems, and SUPER addressing the question of safety of computer
vision algorithms for aerospace applications.
Durée : 12 mois
Salaire net : environ 25 k€ annuel
PROFIL DU CANDIDAT
Formation : PhD in Machine Learning or Computer Vision
Compétences souhaitées :
• Knowledge of Deep Learning approaches (design, frameworks)
• Capacité de publication attestée.
GEN-F213-2 (GEN-SCI-034)