Les r\u00e9seaux antagonistes g\u00e9n\u00e9ratifs (ou GANs), en intelligence artificielle, sont une classe d’algorithmes d’apprentissage non supervis\u00e9. Ils permettent de g\u00e9n\u00e9rer \u00e0 partir d’une base de donn\u00e9es des images parfois abstraites, parfois extr\u00eamement r\u00e9alistes, parfois les deux \u00e0 la fois. Bien que quelques solutions nous permettent contre de l’argent de commencer \u00e0 g\u00e9n\u00e9rer nos propres images en ligne, l’outil reste incompr\u00e9hensible pour le graphiste que je suis. Les images produites par certains d\u00e9veloppeurs sont pourtant int\u00e9ressantes, elles repr\u00e9sentent pour moi une version distordue de la r\u00e9alit\u00e9, une \u00e9trange copie du r\u00e9el, le monde physique simul\u00e9 par un algorithme. Cependant, apr\u00e8s quelques essais de g\u00e9n\u00e9ration et quelques recherches, les GANs, d’abord pr\u00e9sent\u00e9s comme de nouveaux outils de cr\u00e9ation, ne semblent pas vraiment trouver leur place dans l’art. Il est en effet difficile pour un artiste de s’emparer techniquement de l’outil, de le d\u00e9cortiquer. J’ai donc d\u00e9cid\u00e9 d’essayer moi-m\u00eame, j’ai pass\u00e9 beaucoup d’heures \u00e0 lire de la th\u00e9orie et \u00e0 essayer de comprendre les m\u00e9canismes derri\u00e8re cet algorithme et je suis pass\u00e9 \u00e0 la pratique. J’ai donc entrepris de produire une police de caract\u00e8re \u00e0 partir d’un r\u00e9seau antagoniste g\u00e9n\u00e9ratif et d’une large base de donn\u00e9es typographique, afin d’appliquer cet outil \u00e0 un nouvel exercice et ainsi \u00e9valuer le niveau d’accessibilit\u00e9 des GANs pour les artistes. Le but \u00e9tant de juger si l’outil est accessible et adaptable en autodidacte et donc r\u00e9ellement utilisable dans les champs du Design Graphique ou si ce dernier, encore jeune et manquant de documentation, est impossible \u00e0 d\u00e9tourner.<\/p>\n","protected":false},"excerpt":{"rendered":"
Les r\u00e9seaux antagonistes g\u00e9n\u00e9ratifs (ou GANs), en intelligence artificielle, sont une classe d’algorithmes d’apprentissage non supervis\u00e9. Ils permettent de g\u00e9n\u00e9rer \u00e0 partir d’une base de donn\u00e9es des images…<\/p>\n","protected":false},"author":20,"featured_media":4443,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[37,4],"tags":[833,834,832,114,260,835,823],"class_list":["post-4442","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-etudiants","category-projets","tag-base-de-donnee","tag-generation","tag-intelligence-artificiel","tag-monde","tag-realite","tag-simulation","tag-typographie"],"jetpack_featured_media_url":"https:\/\/www.reynalddrouhin.net\/edu\/dataflow\/wp-content\/uploads\/2021\/12\/4fdd066384c9864d46d6.jpg","jetpack_shortlink":"https:\/\/wp.me\/p72hC6-19E","jetpack-related-posts":[{"id":33,"url":"https:\/\/www.reynalddrouhin.net\/edu\/dataflow\/2010\/06\/le-grand-generique-%e2%80%a8de-tous-les-etres-humains\/","url_meta":{"origin":4442,"position":0},"title":"Le Grand G\u00e9n\u00e9rique \u2028de tous les \u00eatres humains","author":"Reynald Drouhin","date":"22 juin 2010","format":false,"excerpt":"http:\/\/www.thegrandcredits.info\/ Un long g\u00e9n\u00e9rique d\u00e9file lentement sur l'\u00e9cran de cin\u00e9ma, de bas en haut. 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