{"id":150,"date":"2025-04-28T22:01:45","date_gmt":"2025-04-29T02:01:45","guid":{"rendered":"https:\/\/businessphysics.ai\/?p=150"},"modified":"2025-04-28T22:01:47","modified_gmt":"2025-04-29T02:01:47","slug":"zero-shot-learning-simply-explained","status":"publish","type":"post","link":"https:\/\/businessphysics.ai\/pt\/zero-shot-learning-simply-explained\/","title":{"rendered":"Aprendizado Zero-Shot (Explica\u00e7\u00e3o simples)"},"content":{"rendered":"<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"904\" height=\"516\" src=\"https:\/\/businessphysics.ai\/wp-content\/uploads\/2025\/04\/ZSL.jpeg\" alt=\"\" class=\"wp-image-151\" srcset=\"https:\/\/businessphysics.ai\/wp-content\/uploads\/2025\/04\/ZSL.jpeg 904w, https:\/\/businessphysics.ai\/wp-content\/uploads\/2025\/04\/ZSL-300x171.jpeg 300w, https:\/\/businessphysics.ai\/wp-content\/uploads\/2025\/04\/ZSL-768x438.jpeg 768w, https:\/\/businessphysics.ai\/wp-content\/uploads\/2025\/04\/ZSL-18x10.jpeg 18w\" sizes=\"auto, (max-width: 904px) 100vw, 904px\" \/><figcaption class=\"wp-element-caption\">Imagem de: https:\/\/encord.com\/blog\/zero-shot-learning-explained\/<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\">O que \u00e9 aprendizagem de tiro zero (ZSL)?<\/h2>\n\n\n\n<p><strong>Aprendizagem de tiro zero (ZSL)<\/strong> \u00e9 uma abordagem de aprendizado de m\u00e1quina que permite que os modelos reconhe\u00e7am e categorizem as coisas que eles t\u00eam <strong>nunca visto antes<\/strong> sem precisar de exemplos rotulados dessas coisas novas durante o treinamento. Diferentemente do aprendizado supervisionado, que exige muitos dados rotulados, que devem ser categorizados explicitamente, a ZSL usa informa\u00e7\u00f5es auxiliares, como <strong>incorpora\u00e7\u00e3o sem\u00e2ntica<\/strong> (que s\u00e3o representa\u00e7\u00f5es num\u00e9ricas do significado de palavras ou conceitos) ou <strong>atributos<\/strong> (caracter\u00edsticas descritivas como cor, forma ou categoria).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\">Como funciona o Zero-Shot Learning?<\/h2>\n\n\n\n<p>Como dissemos anteriormente, a ZSL usa informa\u00e7\u00f5es auxiliares para preencher a lacuna entre o que foi visto (\"classes vistas\") e o que n\u00e3o foi visto (\"classes n\u00e3o vistas\"):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Aulas vistas:<\/strong> Categorias nas quais o modelo \u00e9 treinado, com exemplos rotulados.<\/li>\n\n\n\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Classes invis\u00edveis:<\/strong> Novas categorias que o modelo nunca viu durante o treinamento.<\/li>\n\n\n\n<li><strong>Informa\u00e7\u00f5es auxiliares:<\/strong> Descri\u00e7\u00f5es, atributos ou rela\u00e7\u00f5es sem\u00e2nticas que descrevem classes vis\u00edveis e n\u00e3o vis\u00edveis (por exemplo, \"uma zebra \u00e9 como um cavalo, mas com listras\")<\/li>\n<\/ul>\n\n\n\n<p><strong>Exemplo:<\/strong> Digamos que voc\u00ea tenha treinado seu modelo para reconhecer cavalos e gatos, mas nunca mostrou a ele uma zebra. Por exemplo, voc\u00ea poderia dizer que uma zebra \u00e9 um \"cavalo com listras\". O modelo pode usar o que sabe sobre cavalos e as novas informa\u00e7\u00f5es sobre listras para identificar uma zebra, mesmo que nunca tenha visto uma antes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\">Como \u00e9 o processo?<\/h2>\n\n\n\n<p>O Zero-Shot Learning geralmente consiste em <strong>dois est\u00e1gios principais:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Treinamento:<\/strong> Fazer com que o modelo aprenda com dados rotulados para que ele possa reconhecer aquilo em que foi treinado.<\/li>\n\n\n\n<li><strong>Infer\u00eancia:<\/strong> Quando \u00e9 mostrado ao modelo algo que ele nunca viu antes, ele usa as informa\u00e7\u00f5es auxiliares obtidas na etapa anterior e o novo exemplo para fazer uma previs\u00e3o com base nas semelhan\u00e7as.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-right:0;margin-bottom:var(--wp--preset--spacing--20);margin-left:0\">Por que \u00e9 importante?<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Economiza tempo e recursos: <\/strong>N\u00e3o h\u00e1 necessidade de coletar e rotular enormes conjuntos de dados para cada categoria poss\u00edvel.<\/li>\n\n\n\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Lida com casos raros ou novos:<\/strong> Os modelos podem reconhecer classes novas, raras ou emergentes sem retreinamento.<\/li>\n\n\n\n<li><strong>Flexibilidade no mundo real:<\/strong> \u00datil em \u00e1reas em que novas categorias aparecem com frequ\u00eancia ou em que \u00e9 dif\u00edcil obter dados rotulados, como imagens m\u00e9dicas, processamento de linguagem ou monitoramento da vida selvagem<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\">Aplicativos de aprendizagem Zero-Shot<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Classifica\u00e7\u00e3o de imagens:<\/strong> Reconhecimento de novos tipos de animais ou objetos sem imagens rotuladas.<\/li>\n\n\n\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Processamento de linguagem natural:<\/strong> Compreens\u00e3o de novos t\u00f3picos ou inten\u00e7\u00f5es no texto sem exemplos expl\u00edcitos.<\/li>\n\n\n\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Diagn\u00f3stico m\u00e9dico:<\/strong> Identifica\u00e7\u00e3o de doen\u00e7as raras usando descri\u00e7\u00f5es e rela\u00e7\u00f5es com doen\u00e7as conhecidas.<\/li>\n\n\n\n<li><strong>Avalia\u00e7\u00e3o automatizada:<\/strong> Gera\u00e7\u00e3o de feedback para novos tipos de atribui\u00e7\u00f5es usando o conhecimento de tarefas semelhantes.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\">Desafios da ZSL<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>Qualidade das informa\u00e7\u00f5es auxiliares:<\/strong> O sucesso do modelo depende de qu\u00e3o bem os atributos ou as descri\u00e7\u00f5es capturam as diferen\u00e7as entre as classes.<\/li>\n\n\n\n<li><strong>Limites de generaliza\u00e7\u00e3o:<\/strong> Se as classes n\u00e3o vistas forem muito diferentes das classes vistas, o modelo poder\u00e1 ter dificuldades para fazer previs\u00f5es precisas.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\">Resumo<\/h2>\n\n\n\n<p>A aprendizagem zero-shot capacita os sistemas de IA a dar sentido ao desconhecido, aproveitando relacionamentos, descri\u00e7\u00f5es e atributos, permitindo que eles reconhe\u00e7am coisas novas sem nunca t\u00ea-las visto antes. Isso torna a IA mais adapt\u00e1vel, eficiente e \u00fatil em um mundo em que novos dados e categorias est\u00e3o surgindo constantemente.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Saiba mais sobre o Zero-Shot Learning<\/h3>\n\n\n\n<p style=\"margin-top:var(--wp--preset--spacing--10);margin-bottom:var(--wp--preset--spacing--10)\"><strong>IBM:<\/strong> <a href=\"https:\/\/www.ibm.com\/think\/topics\/zero-shot-learning\">https:\/\/www.ibm.com\/think\/topics\/zero-shot-learning<\/a><br><strong>Grammarly:<\/strong> <a href=\"https:\/\/www.grammarly.com\/blog\/ai\/what-is-zero-shot-learning\/\">https:\/\/www.grammarly.com\/blog\/ai\/what-is-zero-shot-learning\/<\/a><br><strong>V7Labs:<\/strong> <a href=\"https:\/\/www.v7labs.com\/blog\/zero-shot-learning-guide\">https:\/\/www.v7labs.com\/blog\/zero-shot-learning-guide<\/a><br><strong>Registro:<\/strong> <a href=\"https:\/\/encord.com\/blog\/zero-shot-learning-explained\/\">https:\/\/encord.com\/blog\/zero-shot-learning-explained\/<\/a><\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>What is Zero-shot learning (ZSL) ? Zero-shot learning (ZSL) is a machine learning approach that enables models to recognize and categorize things they have never seen before without needing any labeled examples of those new things during training. Unlike supervised learning that require a lot of labeled data, that must be categorized explicitly, ZSL uses [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[34,35],"class_list":["post-150","post","type-post","status-publish","format-standard","hentry","category-ai-article","tag-zero-shot-learning","tag-zsl"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Zero-Shot Learning (Simply Explained) - Business Physics AI Lab<\/title>\n<meta name=\"description\" content=\"What is Zero-Shot Learning ? 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