{"id":2640,"date":"2022-05-14T00:00:42","date_gmt":"2022-05-13T22:00:42","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/?p=2640"},"modified":"2022-09-27T13:32:10","modified_gmt":"2022-09-27T11:32:10","slug":"few-labels-learning","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/few-labels-learning\/","title":{"rendered":"Podcast: Few Labels Learning"},"content":{"rendered":"

Tauchen Sie ein in ein Gespr\u00e4ch mit Jann Goschenhofer \u00fcber das Training von Machine Learning Modellen mit wenigen annotierten Daten.<\/strong><\/p>\n

<\/p>\n

Willkommen zum zweiten Podcast aus der Reihe \u00bbAda wills wissen\u00ab! In dieser Folge spreche ich mit Jann Goschenhofer, Senior Scientist der Gruppe Data Efficient Automated Learning in der Arbeitsgruppe f\u00fcr Supply Chain Services des Fraunhofer IIS und Kompetenzs\u00e4ulenverantwortlicher f\u00fcr \u00bbFew Labels Learning<\/a>\u00ab im ADA Lovelace Center for Analytics, Data and Applications.<\/strong><\/p>\n

Bei Few Labels Learning geht es darum, Machine Learning Modelle zu trainieren, wenn wenig annotierte Daten vorliegen. Im Optimalfall ist der Datensatz f\u00fcr das Training eines Modells gro\u00df und es sind annotierte oder gelabelte Daten vorhanden. Annotiert heisst, die Daten sind mit Informationen versehen. Je gr\u00f6\u00dfer und genauer annotiert der Datensatz ist, desto pr\u00e4ziser kann das Modell auch \u00fcber Dom\u00e4nen hinweg trainiert werden.<\/p>\n

Im Industrieumfeld ist es h\u00e4ufig so, dass zwar viele Daten vorliegen, aber die Annotation nur sp\u00e4rlich oder gar nicht gew\u00e4hrleistet ist. Im Medizinbereich muss eine Datenannotation von Experten durchgef\u00fchrt werden, das kann schnell sehr aufw\u00e4ndig und teuer werden.<\/p>\n

In der Kompetenzs\u00e4ule \u00bbFew Labels Learning<\/a>\u00ab werden verschiedene Methoden f\u00fcr das Lernen mit wenig annotierten Daten erforscht, auf die Jann Goschenhofer im Gespr\u00e4ch n\u00e4her eingeht: Meta-Lernstrategien, Semi-supervised Learning und Datensynthetisierung.<\/p>\n

Diese Methoden k\u00f6nnen in verschiedenen Dom\u00e4nen angewendet werden, von Text \u00fcber Bild zu Videodaten, im X-Ray-Bereich, bis hin zu Zeitreihenanalysen oder Sensordaten. Wenn Sie einen Einblick in die Methoden und praktischen Anwendungsbeispiele erhalten m\u00f6chten, h\u00f6ren Sie gerne in den Podcast rein!<\/p>\n

Wenn Sie unseren Experten Jann Goschenhofer live erleben wollen, haben Sie im KI-Forum der LOGIMAT<\/a> Gelegenheit dazu.<\/p>\nhttps:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/wp-content\/uploads\/2022\/05\/ADA_Podcast_Few_Labels_Learning.mp3<\/a><\/audio>\n

 <\/p>\n

Listen to a conversation with Jann Goschenhofer about training machine learning models with little annotated data.<\/strong><\/p>\n

Welcome to the second podcast in the “Ada wants to know” series! In this episode, I talk with Jann Goschenhofer, Senior Scientist in the Data Efficient Automated Learning group in the Fraunhofer IIS Supply Chain Services research group, and Competence Pillar Leader for \u00bbFew Labels Learning\u00ab in the ADA Lovelace Center for Analytics, Data and Applications.<\/strong><\/p>\n

Few Labels Learning is about training machine learning models when few annotated data is available. The data set for training a model is optimally large and annotated or labeled data is available. Annotated means the data is annotated with information. The larger and more accurately annotated the data set is, the more accurately the model can be trained across domains.<\/p>\n

In the industrial environment, it is often the case that there is a lot of data, but no annotation of the data or not provided at all. In the medical field, data annotation must be done by experts, and this can quickly become very time-consuming and expensive.<\/p>\n

In the \u00bbFew Labels Learning\u00ab competence pillar, various methods for learning with few annotated data are being researched, which Jann Goschenhofer discusses in more detail during the podcast: meta-learning strategies, semi-supervised learning and data synthesis.<\/p>\n

These methods can be applied in various domains, from text to image to video data, in the X-Ray domain, to time series analysis or sensor data. If you want to get an insight into the methods and practical application examples, feel free to listen to this episode!<\/p>\n

If you want to experience our expert Jann Goschenhofer live, you will have the opportunity to do so at the AI Forum at LOGIMAT.<\/p>\n

https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/wp-content\/uploads\/2022\/05\/ADA_Podcast_Few_Labels_Learning.mp4<\/a><\/video><\/div>\n","protected":false},"excerpt":{"rendered":"

Tauchen Sie ein in ein Gespr\u00e4ch mit Jann Goschenhofer \u00fcber das Training von Machine Learning Modellen mit wenigen annotierten Daten.<\/p>\n","protected":false},"author":2,"featured_media":2645,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[54,1,55],"tags":[80,79,72,73,74],"class_list":["post-2640","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-anwendungen","category-forschung","category-menschen","tag-adawillswissen","tag-few-label-learning","tag-machine-learning","tag-metalearning","tag-semi-supervised-learning"],"acf":[],"yoast_head":"\nPodcast: Few Labels Learning - adalovelacecenter-blog<\/title>\n<meta name=\"description\" content=\"In dieser Folge der Podcast-Reige \u00bbADA will\u00b4s wissen\u00ab spricht Jann Goschenhofer \u00fcber Methoden des Few Labels Learnings.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/few-labels-learning\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u00bbAda wills wissen\u00ab Podcast: Few Labels Learning\" \/>\n<meta property=\"og:description\" content=\"In dieser Folge der Podcast-Reige \u00bbADA will\u00b4s wissen\u00ab spricht Jann Goschenhofer \u00fcber Methoden des Few Labels Learnings.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/few-labels-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"adalovelacecenter-blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/de-de.facebook.com\/FraunhoferIIS\" \/>\n<meta property=\"article:published_time\" content=\"2022-05-13T22:00:42+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-09-27T11:32:10+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/wp-content\/uploads\/2022\/05\/Few_Labels_Learning.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1501\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Anik\u00f3 Enderlein\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"\u00bbAda wills wissen\u00ab Podcast: Few Labels Learning\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/wp-content\/uploads\/2022\/05\/Few_Labels_Learning.png\" \/>\n<meta name=\"twitter:creator\" content=\"@FraunhoferIIS\" \/>\n<meta name=\"twitter:site\" content=\"@FraunhoferIIS\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"Anik\u00f3 Enderlein\" \/>\n\t<meta name=\"twitter:label2\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data2\" content=\"3\u00a0Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/few-labels-learning\/\",\"url\":\"https:\/\/websites.fraunhofer.de\/adalovelacecenter-blog\/few-labels-learning\/\",\"name\":\"Podcast: Few Labels Learning - 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