{"id":12977,"date":"2024-08-19T16:57:19","date_gmt":"2024-08-19T21:57:19","guid":{"rendered":"http:\/\/skimai.com\/?p=12977"},"modified":"2024-08-19T16:57:19","modified_gmt":"2024-08-19T21:57:19","slug":"os-5-melhores-trabalhos-de-investigacao-sobre-a-aprendizagem-com-poucos-disparos","status":"publish","type":"post","link":"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/","title":{"rendered":"Os 5 melhores trabalhos de investiga\u00e7\u00e3o sobre a aprendizagem com poucos disparos"},"content":{"rendered":"<p>A aprendizagem com poucos exemplos surgiu como uma \u00e1rea crucial de investiga\u00e7\u00e3o em aprendizagem autom\u00e1tica, com o objetivo de desenvolver algoritmos capazes de aprender com exemplos rotulados limitados. Esta capacidade \u00e9 essencial para muitas aplica\u00e7\u00f5es do mundo real em que os dados s\u00e3o escassos, dispendiosos ou demorados <\/p>\n\n\n<p>Iremos explorar cinco trabalhos de investiga\u00e7\u00e3o seminais que fizeram avan\u00e7ar significativamente o campo da aprendizagem de poucos disparos ao serem implementados. Estes trabalhos introduzem novas abordagens, arquitecturas e protocolos de avalia\u00e7\u00e3o, ultrapassando os limites do que \u00e9 poss\u00edvel neste dom\u00ednio desafiante. Ao examinar essas contribui\u00e7\u00f5es, esperamos fornecer uma vis\u00e3o geral abrangente do estado atual da aprendizagem de poucos disparos e inspirar mais investiga\u00e7\u00e3o nesta \u00e1rea excitante.<\/p>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u00cdndice<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Alternar o \u00edndice\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/#1_Matching_Networks_for_One_Shot_Learning_Vinyals_et_al_2016\" >Redes de correspond\u00eancia para aprendizagem numa \u00fanica oportunidade (Vinyals et al., 2016)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/#2_Prototypical_Networks_for_Few-shot_Learning_Snell_et_al_2017\" >Redes protot\u00edpicas para aprendizagem de poucos disparos (Snell et al., 2017)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/#3_Learning_to_Compare_Relation_Network_for_Few-Shot_Learning_Sung_et_al_2018\" >Aprender a comparar: Rede de rela\u00e7\u00f5es para a aprendizagem de poucos disparos (Sung et al., 2018)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/#4_A_Closer_Look_at_Few-shot_Classification_Chen_et_al_2019\" >Um olhar mais atento \u00e0 classifica\u00e7\u00e3o de poucos disparos (Chen et al., 2019)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/#5_Meta-Baseline_Exploring_Simple_Meta-Learning_for_Few-Shot_Learning_Chen_et_al_2021\" >Meta-Baseline: Explorando a meta-aprendizagem simples para a aprendizagem de poucos disparos (Chen et al., 2021)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/skimai.com\/pt\/top-5-research-papers-on-few-shot-learning\/#The_Evolution_of_Few-Shot_Learning_Simplicity_Insight_and_Future_Directions\" >A evolu\u00e7\u00e3o da aprendizagem de poucos disparos: Simplicidade, perspic\u00e1cia e direc\u00e7\u00f5es futuras<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Matching_Networks_for_One_Shot_Learning_Vinyals_et_al_2016\"><\/span>1. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1606.04080v2\">Redes de correspond\u00eancia para aprendizagem numa \u00fanica oportunidade (Vinyals et al., 2016)<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/57a2b756-e57b-4201-810c-46988bbf2482.png\" alt=\"Trabalho de investiga\u00e7\u00e3o sobre o One Shot Learning\" \/>\n<\/figure>\n\n\n<p>As Redes de Correspond\u00eancia introduziram uma abordagem inovadora \u00e0 aprendizagem de uma s\u00f3 vez, inspirando-se nos mecanismos da mem\u00f3ria e da aten\u00e7\u00e3o. A principal inova\u00e7\u00e3o deste documento \u00e9 a fun\u00e7\u00e3o de correspond\u00eancia, que compara exemplos de consulta com exemplos de suporte rotulados para fazer previs\u00f5es.<\/p>\n\n\n<p>Os autores propuseram um regime de treino epis\u00f3dico que imita o cen\u00e1rio de poucos disparos durante o treino, permitindo que o modelo aprenda a aprender a partir de apenas alguns exemplos. Esta abordagem abriu o caminho para futuros algoritmos de meta-aprendizagem na classifica\u00e7\u00e3o de poucos exemplos. As Matching Networks demonstraram um desempenho impressionante nos conjuntos de dados Omniglot e miniImageNet, estabelecendo um novo padr\u00e3o para m\u00e9todos de aprendizagem de poucos exemplos.<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Prototypical_Networks_for_Few-shot_Learning_Snell_et_al_2017\"><\/span>2. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1703.05175v2\">Redes protot\u00edpicas para aprendizagem de poucos disparos (Snell et al., 2017)<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/7e925e4a-0fab-467c-8fec-181feb4ab18c.png\" alt=\"Artigo de investiga\u00e7\u00e3o sobre a aprendizagem com poucos tiros\" \/>\n<\/figure>\n\n\n<p>Com base no sucesso das Redes de Correspond\u00eancia, as Redes Protot\u00edpicas introduziram uma abordagem mais simples, mas eficaz, para a aprendizagem de poucos disparos. A ideia principal \u00e9 aprender um espa\u00e7o m\u00e9trico no qual as classes podem ser representadas por um \u00fanico prot\u00f3tipo - a m\u00e9dia dos exemplos de suporte incorporados para essa classe.<\/p>\n\n\n<p>As redes protot\u00edpicas utilizam a dist\u00e2ncia euclidiana em vez da semelhan\u00e7a de cosseno, que os autores demonstram ser mais adequada como diverg\u00eancia de Bregman. Esta escolha permite uma interpreta\u00e7\u00e3o probabil\u00edstica clara do modelo. A simplicidade e a efic\u00e1cia das redes protot\u00edpicas tornaram-nas numa base popular para a investiga\u00e7\u00e3o subsequente sobre a aprendizagem de poucos instantes, superando frequentemente os m\u00e9todos mais complexos.<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Learning_to_Compare_Relation_Network_for_Few-Shot_Learning_Sung_et_al_2018\"><\/span>3. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1711.06025v2\">Aprender a comparar: Rede de rela\u00e7\u00f5es para a aprendizagem de poucos disparos (Sung et al., 2018)<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/050788bb-e54a-4bad-98f0-94b622afa50d.png\" alt=\"Artigo de investiga\u00e7\u00e3o sobre a aprendizagem com poucos tiros\" \/>\n<\/figure>\n\n\n<p>As redes de rela\u00e7\u00f5es levaram a abordagem de aprendizagem de m\u00e9tricas dos m\u00e9todos anteriores um passo \u00e0 frente, introduzindo um m\u00f3dulo de rela\u00e7\u00e3o aprend\u00edvel. Em vez de utilizar uma m\u00e9trica fixa como a dist\u00e2ncia euclidiana ou a semelhan\u00e7a de cosseno, as redes de rela\u00e7\u00f5es aprendem a comparar exemplos de consulta e de apoio de uma forma flex\u00edvel.<\/p>\n\n\n<p>O m\u00f3dulo de rela\u00e7\u00e3o \u00e9 implementado como uma rede neural que recebe como entrada as carater\u00edsticas concatenadas de um exemplo de consulta e de apoio, produzindo uma pontua\u00e7\u00e3o de rela\u00e7\u00e3o. Esta abordagem permite que o modelo aprenda uma m\u00e9trica de compara\u00e7\u00e3o que \u00e9 adaptada \u00e0 tarefa espec\u00edfica e \u00e0 distribui\u00e7\u00e3o de dados. As redes de rela\u00e7\u00f5es mostraram um forte desempenho em v\u00e1rios benchmarks de aprendizagem de poucos disparos, demonstrando o poder de aprender a comparar.<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_A_Closer_Look_at_Few-shot_Classification_Chen_et_al_2019\"><\/span>4. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1904.04232v2\">Um olhar mais atento \u00e0 classifica\u00e7\u00e3o de poucos disparos (Chen et al., 2019)<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/badbf4a2-f014-40ff-a525-f2e572c86494.png\" alt=\"Artigo de investiga\u00e7\u00e3o sobre a aprendizagem com poucos tiros\" \/>\n<\/figure>\n\n\n<p>Este documento apresenta uma an\u00e1lise exaustiva dos m\u00e9todos de aprendizagem de poucos disparos existentes, desafiando alguns pressupostos comuns neste dom\u00ednio. Os autores propuseram modelos de base simples que, quando corretamente treinados, podem igualar ou exceder o desempenho de abordagens de meta-aprendizagem mais complexas.<\/p>\n\n\n<p>Uma das principais conclus\u00f5es deste trabalho \u00e9 a import\u00e2ncia da estrutura das carater\u00edsticas e das estrat\u00e9gias de forma\u00e7\u00e3o na aprendizagem com poucos disparos. Os autores mostraram que um classificador padr\u00e3o treinado em todas as classes de base, seguido de uma classifica\u00e7\u00e3o do vizinho mais pr\u00f3ximo em classes novas, pode ser altamente eficaz. Este artigo incentivou os investigadores a considerarem cuidadosamente as suas linhas de base e os protocolos de avalia\u00e7\u00e3o na investiga\u00e7\u00e3o da aprendizagem com poucos disparos.<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Meta-Baseline_Exploring_Simple_Meta-Learning_for_Few-Shot_Learning_Chen_et_al_2021\"><\/span>5. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/2003.04390v4\">Meta-Baseline: Explorando a meta-aprendizagem simples para a aprendizagem de poucos disparos (Chen et al., 2021)<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/ed044b3e-4736-4107-a7b1-a2706100e01c.png\" alt=\"Artigo de investiga\u00e7\u00e3o sobre Meta-Learning\" \/>\n<\/figure>\n\n\n<p>Com base nos conhecimentos de \"A Closer Look at Few-shot Classification\", o Meta-Baseline prop\u00f5e uma abordagem de meta-aprendizagem simples mas altamente eficaz. O m\u00e9todo combina o pr\u00e9-treinamento padr\u00e3o em classes de base com uma fase de meta-aprendizagem que afina o modelo para tarefas de poucas oportunidades.<\/p>\n\n\n<p>Os autores apresentam uma an\u00e1lise detalhada dos compromissos entre os objectivos da forma\u00e7\u00e3o normal e da meta-aprendizagem. Mostram que, embora a meta-aprendizagem possa melhorar o desempenho na distribui\u00e7\u00e3o do treino, pode por vezes prejudicar a generaliza\u00e7\u00e3o para novas classes. A Meta-Baseline alcan\u00e7a o desempenho mais avan\u00e7ado em benchmarks de aprendizagem padr\u00e3o de poucos disparos, demonstrando que as abordagens simples podem ser altamente eficazes quando corretamente concebidas e analisadas.<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Evolution_of_Few-Shot_Learning_Simplicity_Insight_and_Future_Directions\"><\/span>A evolu\u00e7\u00e3o da aprendizagem de poucos disparos: Simplicidade, perspic\u00e1cia e direc\u00e7\u00f5es futuras<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p>Estes cinco artigos inovadores n\u00e3o s\u00f3 fizeram avan\u00e7ar a investiga\u00e7\u00e3o acad\u00e9mica, como tamb\u00e9m abriram caminho para aplica\u00e7\u00f5es pr\u00e1ticas da aprendizagem de poucos dados na IA empresarial. De Matching Networks a Meta-Baseline, assistimos a uma progress\u00e3o no sentido de sistemas de IA mais eficientes e adapt\u00e1veis que podem aprender com dados limitados - uma capacidade crucial em muitos contextos empresariais. Estas inova\u00e7\u00f5es est\u00e3o a permitir que as empresas implementem a IA em cen\u00e1rios em que os dados s\u00e3o escassos ou dispendiosos de obter, como a dete\u00e7\u00e3o de eventos raros, experi\u00eancias personalizadas de clientes e prototipagem r\u00e1pida de novas solu\u00e7\u00f5es de IA. <\/p>\n\n\n<p>A \u00eanfase em modelos mais simples mas eficazes, tal como salientado nos \u00faltimos documentos, alinha-se bem com as necessidades das empresas em termos de sistemas de IA interpret\u00e1veis e pass\u00edveis de manuten\u00e7\u00e3o. \u00c0 medida que as empresas continuam a procurar vantagens competitivas atrav\u00e9s da IA, a capacidade de adaptar rapidamente os modelos a novas tarefas com um m\u00ednimo de dados tornar-se-\u00e1 cada vez mais valiosa. A viagem atrav\u00e9s destes documentos aponta para um futuro em que a IA empresarial pode ser mais \u00e1gil, rent\u00e1vel e reactiva \u00e0s necessidades empresariais em r\u00e1pida mudan\u00e7a, acabando por impulsionar a inova\u00e7\u00e3o e a efici\u00eancia em todos os sectores.<\/p>","protected":false},"excerpt":{"rendered":"<p>Few-shot learning has emerged as a crucial area of research in machine learning, aiming to develop algorithms that can learn from limited labeled examples. This capability is essential for many real-world applications where data is scarce, expensive, or time-consuming to obtain. We will explore five seminal research papers that have significantly advanced the field of [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":13005,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[100,67,134],"tags":[],"class_list":["post-12977","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai","category-ml-nlp","category-research-stats"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top 5 Research Papers on Few-Shot Learning - Skim AI<\/title>\n<meta name=\"description\" content=\"Explore five groundbreaking research papers that have significantly advanced few-shot learning, offering novel approaches and architectures to tackle learning from limited data. 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