{"id":479384,"date":"2023-08-09T10:35:54","date_gmt":"2023-08-09T10:35:54","guid":{"rendered":""},"modified":"2023-09-05T11:18:41","modified_gmt":"2023-09-05T11:18:41","slug":"transfer-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/fr\/wiki\/transfer-learning\/","title":{"rendered":"Transf\u00e9rer l&#039;apprentissage"},"content":{"rendered":"<p>Br\u00e8ves informations sur l&#039;apprentissage par transfert<\/p>\n<p>L&#039;apprentissage par transfert est un probl\u00e8me de recherche en apprentissage automatique (ML) dans lequel les connaissances acquises lors de la formation sur une t\u00e2che sont appliqu\u00e9es \u00e0 un probl\u00e8me diff\u00e9rent mais connexe. Essentiellement, l\u2019apprentissage par transfert permet d\u2019adapter un mod\u00e8le pr\u00e9-entra\u00een\u00e9 \u00e0 un nouveau probl\u00e8me, r\u00e9duisant consid\u00e9rablement le temps de calcul et les ressources. Il contribue \u00e0 am\u00e9liorer l\u2019efficacit\u00e9 de l\u2019apprentissage et peut \u00eatre particuli\u00e8rement utile dans les sc\u00e9narios o\u00f9 les donn\u00e9es sont rares ou co\u00fbteuses \u00e0 obtenir.<\/p>\n<h2>L&#039;histoire de l&#039;origine de l&#039;apprentissage par transfert et sa premi\u00e8re mention<\/h2>\n<p>Le concept d\u2019apprentissage par transfert remonte au domaine de la psychologie des ann\u00e9es 1900, mais il n\u2019a commenc\u00e9 \u00e0 faire des vagues dans la communaut\u00e9 de l\u2019apprentissage automatique qu\u2019au d\u00e9but du 21e si\u00e8cle. Le travail fondateur de Caruana en 1997, \u00ab\u00a0Apprentissage multit\u00e2che\u00a0\u00bb, a jet\u00e9 les bases pour comprendre comment les connaissances acquises lors d&#039;une t\u00e2che pouvaient \u00eatre appliqu\u00e9es \u00e0 d&#039;autres.<\/p>\n<p>Le domaine a commenc\u00e9 \u00e0 prosp\u00e9rer avec l&#039;essor de l&#039;apprentissage profond, avec des avanc\u00e9es notables vers 2010, exploitant des r\u00e9seaux neuronaux pr\u00e9-entra\u00een\u00e9s pour des t\u00e2ches telles que la reconnaissance d&#039;images.<\/p>\n<h2>Informations d\u00e9taill\u00e9es sur l&#039;apprentissage par transfert\u00a0: \u00e9largir le sujet<\/h2>\n<p>L\u2019apprentissage par transfert peut \u00eatre class\u00e9 en trois domaines principaux\u00a0:<\/p>\n<ol>\n<li><strong>Apprentissage par transfert inductif<\/strong>: Apprentissage de la fonction pr\u00e9dictive cible \u00e0 l&#039;aide de quelques donn\u00e9es auxiliaires.<\/li>\n<li><strong>Apprentissage par transfert transductif<\/strong>: Apprentissage de la fonction pr\u00e9dictive cible sous une distribution diff\u00e9rente mais connexe.<\/li>\n<li><strong>Apprentissage par transfert non supervis\u00e9<\/strong>: Transf\u00e9rer l\u2019apprentissage o\u00f9 les t\u00e2ches source et cible ne sont pas supervis\u00e9es.<\/li>\n<\/ol>\n<p>C&#039;est devenu une technique vitale pour former des mod\u00e8les d&#039;apprentissage profond, en particulier lorsque les donn\u00e9es \u00e9tiquet\u00e9es disponibles pour une t\u00e2che sp\u00e9cifique sont limit\u00e9es.<\/p>\n<h2>La structure interne de l\u2019apprentissage par transfert\u00a0: comment fonctionne l\u2019apprentissage par transfert<\/h2>\n<p>L&#039;apprentissage par transfert fonctionne en prenant un mod\u00e8le pr\u00e9-entra\u00een\u00e9 (une source) sur un grand ensemble de donn\u00e9es et en l&#039;adaptant \u00e0 une nouvelle t\u00e2che cible connexe. Voici comment cela se d\u00e9roule g\u00e9n\u00e9ralement\u00a0:<\/p>\n<ol>\n<li><strong>S\u00e9lection d&#039;un mod\u00e8le pr\u00e9-entra\u00een\u00e9<\/strong>: Un mod\u00e8le entra\u00een\u00e9 sur un grand ensemble de donn\u00e9es.<\/li>\n<li><strong>R\u00e9glage fin<\/strong>: Ajuster le mod\u00e8le pr\u00e9-entra\u00een\u00e9 pour le rendre adapt\u00e9 \u00e0 la nouvelle t\u00e2che.<\/li>\n<li><strong>Reconversion<\/strong>: Entra\u00eenement du mod\u00e8le modifi\u00e9 sur le plus petit ensemble de donn\u00e9es li\u00e9 \u00e0 la nouvelle t\u00e2che.<\/li>\n<li><strong>\u00c9valuation<\/strong>: Test du mod\u00e8le recycl\u00e9 sur la nouvelle t\u00e2che pour \u00e9valuer les performances.<\/li>\n<\/ol>\n<h2>Analyse des principales caract\u00e9ristiques de l&#039;apprentissage par transfert<\/h2>\n<ul>\n<li><strong>Efficacit\u00e9<\/strong>: R\u00e9duit consid\u00e9rablement le temps de formation.<\/li>\n<li><strong>Polyvalence<\/strong>: Peut \u00eatre appliqu\u00e9 \u00e0 divers domaines, notamment les images, le texte et l&#039;audio.<\/li>\n<li><strong>Am\u00e9lioration des performances<\/strong>: Surclasse souvent les mod\u00e8les form\u00e9s \u00e0 partir de z\u00e9ro sur la nouvelle t\u00e2che.<\/li>\n<\/ul>\n<h2>Types d&#039;apprentissage par transfert\u00a0: utiliser des tableaux et des listes<\/h2>\n<table>\n<thead>\n<tr>\n<th>Taper<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Inductif<\/td>\n<td>Transf\u00e8re les connaissances entre des t\u00e2ches diff\u00e9rentes mais li\u00e9es<\/td>\n<\/tr>\n<tr>\n<td>Transductif<\/td>\n<td>Transf\u00e8re les connaissances entre des distributions diff\u00e9rentes mais li\u00e9es<\/td>\n<\/tr>\n<tr>\n<td>Sans surveillance<\/td>\n<td>S&#039;applique aux t\u00e2ches d&#039;apprentissage non supervis\u00e9es<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Fa\u00e7ons d&#039;utiliser l&#039;apprentissage par transfert, les probl\u00e8mes et leurs solutions<\/h2>\n<ul>\n<li><strong>Utilisation dans diff\u00e9rents domaines<\/strong>: Reconnaissance d&#039;images, traitement du langage naturel, etc.<\/li>\n<li><strong>D\u00e9fis<\/strong>: S\u00e9lection des donn\u00e9es pertinentes, risque de transfert n\u00e9gatif.<\/li>\n<li><strong>Solutions<\/strong>: S\u00e9lection rigoureuse des mod\u00e8les sources, r\u00e9glage des hyperparam\u00e8tres.<\/li>\n<\/ul>\n<h2>Principales caract\u00e9ristiques et autres comparaisons sous forme de tableaux et de listes<\/h2>\n<table>\n<thead>\n<tr>\n<th>Caract\u00e9ristique<\/th>\n<th>Apprentissage par transfert<\/th>\n<th>Apprentissage traditionnel<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Temps de formation<\/td>\n<td>Plus court<\/td>\n<td>Plus long<\/td>\n<\/tr>\n<tr>\n<td>Exigences en mati\u00e8re de donn\u00e9es<\/td>\n<td>Moins<\/td>\n<td>Plus<\/td>\n<\/tr>\n<tr>\n<td>La flexibilit\u00e9<\/td>\n<td>Haut<\/td>\n<td>Faible<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perspectives et technologies du futur li\u00e9es \u00e0 l&#039;apprentissage par transfert<\/h2>\n<p>L\u2019apprentissage par transfert devrait se d\u00e9velopper avec les progr\u00e8s de l\u2019apprentissage non supervis\u00e9 et auto-supervis\u00e9. Les technologies futures pourraient voir appara\u00eetre des m\u00e9thodes d\u2019adaptation plus efficaces, des applications inter-domaines et une adaptation en temps r\u00e9el.<\/p>\n<h2>Comment les serveurs proxy peuvent \u00eatre utilis\u00e9s ou associ\u00e9s \u00e0 l&#039;apprentissage par transfert<\/h2>\n<p>Les serveurs proxy comme ceux fournis par OneProxy peuvent faciliter l&#039;apprentissage par transfert en permettant une r\u00e9cup\u00e9ration efficace des donn\u00e9es pour la cr\u00e9ation de grands ensembles de donn\u00e9es. La collecte de donn\u00e9es s\u00e9curis\u00e9e et anonyme garantit le respect des normes \u00e9thiques et des r\u00e9glementations locales.<\/p>\n<h2>Liens connexes<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.csd.uwo.ca\/~yuri\/Papers\/tkde.pdf\" target=\"_new\" rel=\"noopener nofollow\">Une enqu\u00eate sur l&#039;apprentissage par transfert<\/a><\/li>\n<li><a href=\"https:\/\/cs231n.github.io\/transfer-learning\/\" target=\"_new\" rel=\"noopener nofollow\">Cours de Stanford sur l&#039;apprentissage par transfert<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/fr\/\" target=\"_new\" rel=\"noopener\">OneProxy\u00a0: serveurs proxy pour la collecte de donn\u00e9es<\/a><\/li>\n<\/ul>","protected":false},"featured_media":470725,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479384","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Transfer Learning<\/mark>","faq_items":[{"question":"What is Transfer Learning?","answer":"<p>Transfer Learning is a technique in machine learning where a model developed for one task is reused as the starting point for a model on a second task. It's about taking a pre-trained model (trained on some large dataset) and fine-tuning it for a new, related problem, thereby saving computation time and resources.<\/p>"},{"question":"How did Transfer Learning originate?","answer":"<p>Transfer Learning can be traced back to the field of psychology in the 1900s, but its application in machine learning began with the work of Caruana in 1997. The growth of deep learning around 2010 further facilitated its widespread adoption in tasks like image recognition.<\/p>"},{"question":"What are the main types of Transfer Learning?","answer":"<p>There are three main types of Transfer Learning: Inductive, where knowledge is transferred across different but related tasks; Transductive, where knowledge is transferred across different but related distributions; and Unsupervised, which applies to unsupervised learning tasks.<\/p>"},{"question":"How does Transfer Learning work?","answer":"<p>Transfer Learning works by taking a pre-trained model on a large dataset and adapting it for a new, related target task. This typically involves selecting a pre-trained model, fine-tuning it, re-training it on the smaller dataset related to the new task, and then evaluating its performance.<\/p>"},{"question":"What are the key features of Transfer Learning?","answer":"<p>The key features of Transfer Learning include its efficiency in reducing training time, versatility across various domains, and often providing a performance boost over models trained from scratch on a new task.<\/p>"},{"question":"What problems might be encountered with Transfer Learning, and how can they be solved?","answer":"<p>Some challenges in Transfer Learning include the selection of relevant data and the risk of negative transfer, where the transfer might hinder instead of help the learning process. These challenges can be overcome by careful selection of source models and proper hyperparameter tuning.<\/p>"},{"question":"How are proxy servers like OneProxy associated with Transfer Learning?","answer":"<p>Proxy servers like those provided by OneProxy can facilitate Transfer Learning by enabling efficient data scraping for building large datasets. This secure and anonymous data collection ensures compliance with ethical standards and local regulations.<\/p>"},{"question":"What are the future perspectives and technologies associated with Transfer Learning?","answer":"<p>Future perspectives related to Transfer Learning include growth in unsupervised and self-supervised learning, more efficient adaptation methods, cross-domain applications, and real-time adaptation.<\/p>"},{"question":"How does Transfer Learning compare to traditional learning methods?","answer":"<p>Compared to traditional learning, Transfer Learning typically requires shorter training time, fewer data requirements, and offers higher flexibility. It can often provide better performance on new tasks compared to models trained from scratch.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/479384","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/479384\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media\/470725"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media?parent=479384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}