Commit 2d8b2cbf authored by Antoine Caubrière's avatar Antoine Caubrière
Browse files

ajout date de soutenance + correction chaine

parent 64105b89
......@@ -237,7 +237,7 @@ Ils nous montrent que le mode étoile couplé à un modèle de langage 5-gramme
En complément, nous pouvons déduire de ces résultats qu'il est préférable d'appliquer notre mode étoile pour l'ensemble de la tâche d'extraction des concepts sémantiques, c'est-à-dire dès l'augmentation de données réalisée avec PORTMEDIA.
Jusqu'ici, notre meilleure approche de bout en bout ne permet pas d'atteindre des performances aussi compétitives qu'une approche par chaine de composants.
Jusqu'ici, notre meilleure approche de bout en bout ne permet pas d'atteindre des performances aussi compétitives qu'une approche par chaîne de composants.
Notre approche n'utilise que le signal audio et un modèle de langage de type n-gramme.
Alors que notre meilleure chaîne de composants est enrichie via des traitements de langage naturel par des caractéristiques linguistiques, notre approche n'intègre pas explicitement ce type de informations.
......@@ -644,7 +644,7 @@ En modifiant la profondeur de l'architecture neuronale, en bénéficiant de l'ap
Il s'agit des meilleurs résultats pour la tâche d'extraction des concepts sémantiques MEDIA obtenus dans cette thèse.
Nous proposons dans la sous-section suivante d'effectuer leur comparaison au meilleur résultat d'une chaîne de composants.
\subsection{Comparaison de l'approche proposée avec une approche par chaine de composants}
\subsection{Comparaison de l'approche proposée avec une approche par chaîne de composants}
Notre meilleure approche de bout en bout exploite pleinement toutes les contributions que nous avons apportées dans le cadre de cette thèse.
......@@ -693,7 +693,7 @@ Nous avons par la suite tenté d'agir sur la profondeur de notre modèle neurona
Ce modèle couplé aux méthodes proposées dans cette thèse nous permet d'obtenir nos meilleurs résultats sur la tâche MEDIA.
L'approche finale que nous mettons en \oe{}uvre nous permet d'obtenir des performances légèrement supérieures à celles d'une approche par chaîne de composants à jour, même si ces différences ne sont pas significatives.
Il est intéressant de noter que notre approche n'exploite que le signal audio et un modèle de langage pour obtenir ces performances, alors que l'approche par chaine de composants bénéficie de l'enrichissement d'informations linguistiques obtenues à l'aide d'outil externe de traitement automatique du langage naturel.
Il est intéressant de noter que notre approche n'exploite que le signal audio et un modèle de langage pour obtenir ces performances, alors que l'approche par chaîne de composants bénéficie de l'enrichissement d'informations linguistiques obtenues à l'aide d'outil externe de traitement automatique du langage naturel.
Les travaux de ce chapitre concernant notre application d'une stratégie de curriculum ont conduit aux publications scientifiques~[\cite{caubriere:hal-02304597,caubriere:hal-02304614}].
......
......@@ -44,7 +44,7 @@
%\lesoustitre{« Sous-titre de la th\`{e}se »}
%Indiquer la date et le lieu de soutenance de la th\`{e}se
%indicates the date and the place of the defense
\date{XXXX}
\date{29 Janvier 2021}
\lieu{Le Mans Université, LIUM}
%Indiquer le nom du (ou des) laboratoire (s) dans le(s)quel(s) le travail de th\`{e}se a \'{e}t\'{e} effectu\'{e}, indiquer aussi si souhait\'{e} le nom de la (les) facult\'{e}(s) (UFR, \'{e}cole(s), Institut(s), Centre(s)...), son (leurs) adresse(s)...
%Indicates the name (or names) of research laboratories where the work has been done as well as (if desired) the names of faculties (UFR, Schools, institution...
......
......@@ -1336,7 +1336,7 @@
\newlabel{res:prof_mix}{{6.10}{143}{Résultats expérimentaux exprimés en CER et CVER suite à la modification du nombre de couches cachées en cours d'entraînement. Les résultats encadrés par des guillemets sont reportés de la table \ref {res:prof_567}}{table.6.10}{}}
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\newlabel{res:prof_mix_stars}{{6.11}{144}{Résultats expérimentaux exprimés en CER et CVER pour des chaînes d'apprentissages optimisées en profondeur et l'utilisation du mode étoile}{table.6.11}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {6.3.1}Comparaison de l'approche propos\IeC {\'e}e avec une approche par chaine de composants}{144}{subsection.6.3.1}}
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\@writefile{lot}{\defcounter {refsection}{0}\relax }\@writefile{lot}{\contentsline {table}{\numberline {6.12}{\ignorespaces Comparaison des approches \IeC {\`a} cha\IeC {\^\i }nes de composants et de bout en bout. Report des meilleurs r\IeC {\'e}sultats de chaque approche obtenus dans le cadre de cette th\IeC {\`e}se.}}{144}{table.6.12}}
\newlabel{res:comp_soa}{{6.12}{144}{Comparaison des approches à chaînes de composants et de bout en bout. Report des meilleurs résultats de chaque approche obtenus dans le cadre de cette thèse}{table.6.12}{}}
\abx@aux@cite{caubriere:hal-02304597}
......@@ -1704,7 +1704,6 @@
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......@@ -104,7 +104,7 @@
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\BOOKMARK [1][-]{section.6.3}{Impact de la profondeur du mod\350le}{chapter.6}% 106
\BOOKMARK [2][-]{subsection.6.3.1}{Comparaison de l'approche propos\351e avec une approche par chaine de composants}{section.6.3}% 107
\BOOKMARK [2][-]{subsection.6.3.1}{Comparaison de l'approche propos\351e avec une approche par cha\356ne de composants}{section.6.3}% 107
\BOOKMARK [1][-]{section.6.4}{Conclusion}{chapter.6}% 108
\BOOKMARK [0][-]{chapter.7}{Analyse d'erreurs et exploitation de repr\351sentations internes}{part.2}% 109
\BOOKMARK [1][-]{section.7.1}{Contexte de l'analyse}{chapter.7}% 110
......
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......@@ -291,7 +291,7 @@
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......
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