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, Expériences et Contributions dans le passé, un modèle doit être capable d'apprendre à classer de nouvelles occurrences des ces catégories

, En effet, la plupart des techniques d'apprentissage nécessitant un large ensemble de données statistiquement représentatives de la réalité ne peuvent s'adapter à cette situation extrême. De nouveaux modèles sont dont nécessaire pour exploiter au mieux les exemples d'apprentissage. Des travaux essaie notamment de tirer parti de la robustesse et de la flexibilité des réseaux de neurones pour cette tâche, tout essayant d'éviter les problème de sur-apprentissage

, En effet, un utilisateur pourrait par exemple programmer un robot assistant pour réagir à ses commandes de manière similaire aux systèmes vocaux actuellement. Cette thèse étudie ce paradigme d'apprentissage et de test dans le cas de gestes isolés, c'est à dire que le modèle doit prédire la classe pour des séquences, L'apprentissage one-shot présente des perspective d'utilisations très intéressante pour la reconnaissance de gestes, et c'est la raison pour laquelle nous avons décidé de l'étudier

, Comparé aux paradigmes d'apprentissage et de classification traditionnels, le one-shot reste jusqu'à aujourd'hui un domaine assez peu étudié, avec néanmoins une activité de recherche croissante. Par exemple, il n'existe à notre connaissance qu'une seule contribution sur l'utilisation de réseaux de neurones pour des données séquentielles, 2016.

. Santoro, ont cependant définit un cadre d'étude et une méthodologie plus précise pour étudier le sujet, ce qui permet de définir des objectifs tangibles à accomplir et de canaliser l'effort de recherche sur ce domaine, Cette situation s'explique notamment par la difficulté de la tâche qui requiert des techniques non-conventionnelles. Les publications récentes, 2016.

, Expériences et Contributions

, sur des séquence possède un état de l'art très fourni avec de nombreuses applications déjà disponibles auprès du grand public. Le domaine reste cependant très actif et continu de s'améliorer ou de se diversifier

. Apprentissage-"en-un and . Coup, Dans son mode de fonctionnement one-shot, la reconnaissance a lieu dans un contexte épisodique caractérisé par la succession d'étapes suivantes : 1. tirage d'un vocabulaire í²± ep

, tirage d'un (éventuellement plus) exemple d'apprentissage pour chaque classe de í²± ep , donnant ainsi lieu à un ensemble d'apprentissage (í±‹ ep

, entrainement d'un classifieur sur (í±‹ ep

, Elle a donc aussi lieu lors de a phase de test pour évaluer les performance d'un modèle. L'objectif n'est donc pas de maximiser les performances pour un épisode particulier mais pour n'importe quelle combinaison de classes í²± ep à distinguer les unes des autres

, Pour la majorité des modèles, cette étape de préparation est incontournable pour le succès de la reconnaissance, tandis que les données épisodiques ne servent qu'à spécialiser le modèle en pratique. La littérature sur le sujet apporte plusieurs implémentations possibles, comme le transfert d'apprentissage depuis un modèle entraîné sur tâche différente d'un domaine connexe. Un exemple de transfert est l'adaptation des travaux de vérification pour la biométrie. Pour toute base de données compatible avec la classification en apprentissage one-shot, il est possible de constituer un sous-problème de vérification en tirant des paires d'exemples issues de classes tirée au hasard. Lorsque la taille de l'ensemble d'apprentissage (nombre d'exemples et de classes) est suffisante, on peut aussi entrainer directement le modèle sur des épisodes en veillant toujours à isoler les catégories utilisée de celles réservée au tests. Nous expérimentons ces deux approches ainsi qu'une troisième solution dérivée de la vérification, avec pour objectif d'apprendre des gestes issus du langage des signes. Un lexique de signes enregistrés dans des vidéos nous offre en effet un nombre de classes suffisant, Il est à noter que cette procédure de test épisodique est souvent précédée d'une autre phase d'apprentissage en amont qui prépare le modèle et facilite le travail de l'étape 3 dans laquelle le faible nombre d'exemple limite la quantité d'information disponible, 2000.

, La plupart des publications sur l'apprentissage one-shot faisant appel à des réseaux de neurones utilisent une stratégie commune : un réseau de neurone est entrainé pour générer un