BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen - ECPv5.7.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
X-ORIGINAL-URL:https://www.greyc.fr
X-WR-CALDESC:évènements pour GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240411T140000
DTEND;TZID=Europe/Paris:20240411T153000
DTSTAMP:20260422T152328
CREATED:20240326T130423Z
LAST-MODIFIED:20240326T130624Z
UID:11464-1712844000-1712849400@www.greyc.fr
SUMMARY:Séminaire IMAGE : Multi-domain translation method with little supervision\, applied to medical scans and face generation (Tsiry Mayet)
DESCRIPTION:Nous aurons le plaisir d’accueillir Tsiry Mayet\, de l’INSA Rouen.\nIl donnera un séminaire IMAGE\, le jeudi 11 avril 2024\, à 14h00\, en salle de séminaire F-200.\nTitre: Multi-domain translation method with little supervision\, applied to medical scans and face generation.\nRésumé: \n\nMulti-domain translation is the task of learning a translation between any partition of domains within a set (such as (D1\, D2) → D3\, D2 → (D1\, D3)\, D3 → D1\, etc. for 3 domains) without the need to train separate models for each configuration.\nThe more domains considered\, the harder it is to achieve full supervision\, especially when human intervention is required to label data.\nSemi-supervised multi-domain translation involves a setting where arbitrary domains may be missing during training.\nFor this purpose\, we introduce Multi-Domain Diffusion (MDD). The purpose of this method is twofold: i) to be able to reconstruct any missing views for any new data object and ii) to learn in a semi-supervised context where any configuration of supervision is possible.\nThis is done in Multi-Domain Diffusion (MDD) by exploiting the noise formulation in diffusion models and modeling one noise level per domain. Similar to existing approaches\, MDD learns the translation between any partition of domains. Unlike existing approaches\, our formulation handles semi-supervised context without modification by modeling missing views as noise in the diffusion process.
URL:https://www.greyc.fr/event/seminaire-image-multi-domain-translation-method-with-little-supervision-applied-to-medical-scans-and-face-generation-tsiry-mayet/
LOCATION:ENSICAEN – Batiment F – Salle F-200\, 6 Bd Maréchal Juin\, Caen\, 14050\, France
CATEGORIES:Image,Seminaire Image
END:VEVENT
END:VCALENDAR