{"id":71732,"date":"2023-01-26T15:51:00","date_gmt":"2023-01-26T14:51:00","guid":{"rendered":"https:\/\/www.clickworker.com\/?page_id=71732"},"modified":"2023-03-02T12:56:25","modified_gmt":"2023-03-02T11:56:25","slug":"transfer-learning","status":"publish","type":"page","link":"https:\/\/www.clickworker.com\/ai-glossary\/transfer-learning\/","title":{"rendered":"Transfer Learning"},"content":{"rendered":"<style>\r\n.faq-snippet.collapsable div h2, .faq-snippet {color: #1d1d1f;} .faq-snippet p { color: #212529;}\r\n<\/style>\r\n\r\n<section class=\"white section-space top_grey_separation_sm\">\r\n<div class=\"container pb-5\">\r\n<div class=\"row align-items-center justify-content-center justify-content-lg-between\">\r\n<div class=\"col-lg-6 pb-2\">\r\n\r\n<span class=\"gl_letter_offcanvas gl_single\">T<\/span>\r\n<h1 class=\"section-headline pt-4 mt-1 mb-4\"><span class=\"gl_single_headline\">Transfer Learning<\/span><\/h1>\r\n\r\n<ul>\r\n \t<li><a href=\"#explanantion\">Transfer Learning\u2013 Short Explanation<\/a><\/li>\r\n \t<li><a href=\"#what\">What is transfer learning?<\/a><\/li>\r\n\t<li><a href=\"#how\">How does transfer learning work? <\/a><\/li>\r\n\t<li><a href=\"#inductive\">Define Inductive transfer<\/a><\/li>\r\n\t<li><a href=\"#deeplearning\">Transfer deep learning types<\/a><\/li>\r\n\t<li><a href=\"#applications\">Transfer learning applications <\/a><\/li>\r\n\t<li><a href=\"#advantages\">Advantages and challenges of transfer learning<\/a><\/li>\r\n\t<li><a href=\"#limitations\">Limitations of transfer learning <\/a><\/li>\r\n\t<li><a href=\"#history\">History and future of transfer learning <\/a><\/li>\r\n\t<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\r\n\t<li><a href=\"#faq\">FAQs<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n\r\n<div class=\"col-lg-6\">\r\n\t\t\t <a href=\"https:\/\/www.clickworker.com\/\"><img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/uploads\/2023\/01\/transfer-learning.png\" alt=\"transfer learning\" class=\"gl_image max-width-100\"\/><\/a><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"gray section-space\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"explanantion\">Transfer Learning\u2013 Short Explanation<\/h2>\r\n<p>Transfer learning is a <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/machine-learning\/\">machine learning<\/a> phenomenon where knowledge gained from solving one problem is applied to another similar problem. For instance, a machine learning model trained to classify images to identify a bird in an image can be reused to recognize any other object, like animals in an image.<br>\r\n\r\nTransfer learning is thus defined as reusing information collected from a previous machine learning task. This method is getting popular as it allows for quickly developing models, even when there is little data available. It is used in several computer vision and natural language processing applications.<br>\r\n\r\nThe concept of transfer learning can also be related to other problems, such as <a href=\"https:\/\/en.wikipedia.org\/wiki\/Multi-task_learning\" target=\"_blank\" rel=\"noopener\">multi-task learning<\/a> and <a href=\"https:\/\/en.wikipedia.org\/wiki\/Concept_drift\" target=\"_blank\" rel=\"noopener\">concept drift<\/a>, besides its use in machine learning.<\/p>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"white section-space bottom_grey_separation_sm\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"what\">What is transfer learning<\/h2>\r\n<p>Machine learning models take a long time to train and require huge volumes of data to gain a good level of accuracy in their results. As most AI and machine learning tasks try to mimic how the human brain and knowledge transfer work, it makes sense to think of a solution where learnings from one model can be applied to another.<br>\r\n\r\nHumans normally educate themselves about various subjects, can correlate their learnings, and apply a similar solution to multiple problems. For instance, when someone learns how to ride a bicycle, it becomes easier to learn to ride a motorbike. When the same technique is adapted for machine learning, it is called transfer learning. For instance, a machine learning model trained to recognize food items in an image can be reused to create a model that can also detect drinks from images. <br>\r\n\r\nTransfer learning thus helps go beyond the isolated learning taken up by machine learning models and allows one to use acquired knowledge from one task to another similar task. <br>\r\n\r\nThis transfer learning approach is quite popular with <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/deep-learning\/\">deep learning<\/a> models, where many complex problems are worked upon with the help of neural network models.<br>\r\n\r\nGiven its wide range of applicability and ongoing research, the terms &#8212; learning to learn, knowledge consolidation, and inductive transfer &#8212; are all used synonymously to denote transfer learning.<\/p>\r\n\r\n<p><strong>Video on Transfer Learning<\/strong><\/p>\r\n<div class=\"row\">\r\n<div class=\"col-12 col-lg-8 mx-auto\">\r\n<div class=\"lyte-wrapper\" title=\"What is Transfer Learning? [Explained in 3 minutes]\" style=\"width:420px;max-width:100%;margin:5px;\"><div class=\"lyMe\" id=\"WYL_vmjP6LjGaag\" itemprop=\"video\" itemscope itemtype=\"https:\/\/schema.org\/VideoObject\"><div><meta itemprop=\"thumbnailUrl\" content=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FvmjP6LjGaag%2Fhqdefault.jpg\" \/><meta itemprop=\"embedURL\" content=\"https:\/\/www.youtube.com\/embed\/vmjP6LjGaag\" \/><meta itemprop=\"duration\" content=\"PT3M26S\" \/><meta itemprop=\"uploadDate\" content=\"2022-10-19T12:09:31Z\" \/><\/div><div id=\"lyte_vmjP6LjGaag\" data-src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FvmjP6LjGaag%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\" itemprop=\"name\">What is Transfer Learning? [Explained in 3 minutes]<\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/vmjP6LjGaag\" rel=\"nofollow\"><img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FvmjP6LjGaag%2F0.jpg\" alt=\"What is Transfer Learning? [Explained in 3 minutes]\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><meta itemprop=\"description\" content=\"In this video, we are bringing you a short but effective explanation of what Transfer Learning is and how it works. By formal definition, \u201cTransfer Learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.\u201d Do you have three minutes to spare? \ud83e\udd14 Watch the video to find out what this actually means - with two easy-to-understand examples! #TransferLearning #ML #AI #explained #nocode #automation __________________________________________ Levity is an AI software that allows you to create custom models to train for image, document, and text classification. Levity is completely no-code, meaning you don\u2019t need to have any prior knowledge of machine learning or coding algorithms! Our tool is intended for non-technical process managers who are part of small- and mid-size organizations and banish repetitive and mind-numbing tasks from their workdays entirely. Give us a follow on socials! We&#039;re active almost every day and love chatting to our community members! \ud83d\udc68\u200d\ud83c\udfeb JOIN OUR WEEKLY DEMO: https:\/\/levity.ai\/webinar \ud83d\udc49 SIGN UP FOR LEVITY + tell us about your use case!: https:\/\/levity.ai\/ \ud83d\udc26 Twitter: https:\/\/twitter.com\/levityai \ud83d\udcd8 Facebook: https:\/\/www.facebook.com\/levityai \ud83d\udcbc LinkedIn: https:\/\/www.linkedin.com\/company\/levityai \u23e9 YouTube: https:\/\/www.youtube.com\/c\/LevityAI\"><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px;\"><\/div>\r\n<\/div>\r\n<\/div>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"how\">How does transfer learning work?<\/h2>\r\n<p>Transfer learning can be simplified into the following steps:<\/p>\r\n<ul><li>Train a machine learning model to solve a problem or execute a particular task. Say, recognize birds from a picture.<\/li>\r\n<li>The knowledge gained from this model is used to execute another task B, say recognizing animals from a picture. The learned weights from the network gained while training for task A are transferred to task B, accelerating the model training process for task B. The knowledge transferred from task A to task B could vary depending on the problem and available data. It can be the model fit, it can be training data features, it can be the algorithms used, and so on.<\/li>\r\n<li>This is advantageous, especially in cases where task B has little data to work with. The learnings from task A can be exploited without training the model for task B with enormous amounts of data that would be otherwise needed.<\/li>\r\n<li>Given the conceptual nature of this technique, it has a wider scope beyond machine learning. It can also be seen as a design methodology in fields such as active learning. It is also applied in the field of cognitive science.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"inductive\">Inductive transfer<\/h2>\r\n<p>The <a href=\"https:\/\/link.springer.com\/referenceworkentry\/10.1007\/978-0-387-30164-8_401\" target=\"_blank\" rel=\"noopener\">inductive transfer<\/a> is the process by which general features suitable for both the base and target tasks are transferred from the base network to a target network. The base network is first trained on the base dataset, and then the results and learned features from this task are repurposed for the target network.<br> \r\n\r\nThis type of transfer learning is used in deep learning models. The scope of the possible target models can be figured out by using a model fit of a base model or task. Inductive transfer improves the learning process of the target model by narrowing down the model bias.<br>   \r\n\r\nTo make use of transfer learning in predictive modeling, you can follow any of the two common approaches<\/p>\r\n\r\n<ul><li>Develop model approach<br>\r\nIn this approach, a base or source task is first identified and developed into a model. This model is then used as the base to create a model fit upon which several other models can be adapted or refined from.<\/li>\r\n\r\n<li>Pre-trained model approach<br>\r\nIn this approach, an existing pre-trained source model is selected and used as a base upon which the secondary models are built. This is the popular approach used in many deep-learning models.<\/li>\r\n\r\n<li>Feature extraction<br>\r\nAnother approach to implementing transfer learning is to gather the most important features of a problem and then use them to create a better model. This approach is also called representation learning and helps improve the model design and performance compared to manual feature extractions.<br> \r\n\r\nWhile capable of figuring out the best representative features, Neural networks can still use transfer learning to save up on computing resources and time. This approach also helps reduce data size and overall computation time, making it suitable for traditional algorithms.<br> \r\n\r\nProgramming-wise, developers can make use of the available transfer learning algorithms and libraries such as ADAP (Python), TLib (Python), and Domain Adaptation Toolbox (Matlab).<br>   \r\n\r\nWhile traditional machine learning systems learn from only their input dataset, transfer learning allows for knowledge gained from a previous system and the input dataset to be used for developing a model. Thus compared to the isolated learning carried out by individual models, transfer learning can leverage knowledge and solve problems with less data, computational power, and a shorter period.<br>   \r\n\r\n\r\nMathematically transfer learning can be described with the following representation:<br>  \r\n\r\nConsider a domain D with two components D = {x, P(x)}  <br> \r\n\r\nX represents the feature space and P(X) the marginal distribution of it. I.e P(X) , X = { x1,&#8230;., xn} <br>  \r\n\r\nXi represents the individual vector. If a task to be completed by the model is denoted as T it can be defined with two components  <br> \r\n\r\nT ] {Y, P(Y|X)} = { Y, \u03b7} , Y = { y1, \u2026., un}<br>   \r\n\r\nHere Y is the label space and \u03b7 represents the predictive function.<br>   \r\n\r\nWith these representations, Sebastian Ruder explains transfer learning as <br>  \r\n\r\n<strong>\u201cGiven a source domain Ds , a corresponding source task Ts, as well as a target domain Dt, and a target task Tt, the objective of transfer learning now is to enable us to learn the target conditional probability distribution P(Yt| Xt) in Dt with the information gained from Ds and Ts where  Ds  \u2260 Dt  or Ts  \u2260 Tt\u201d<\/strong> <\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"deeplearning\">Transfer deep learning types<\/h2>\r\n<ul><li>Domain adaptation<br>\r\nWhen a suture domain with different feature spaces and distributions is adapted for another target domain, it is referred to as domain adaptation. This type of learning can be seen in computer vision projects.<\/li>\r\n\r\n<li>Domain confusion<br>\r\nTo make the source and target domains resemble each other to at least some degree, a sort of objective is added to the source domain. This introduced confusion can help make the samples more similar and help with easier transfer learning across the domains.<\/li>\r\n\r\n<li>Multi-task learning<br>\r\nIn this type of learning, multiple tasks are learned simultaneously without distinguishing between the source and targets. This helps develop a richer combined feature vector that can be applied to various problems within the domain, allowing for a shared knowledge system.<\/li>\r\n\r\n<li>One-shot learning<br>\r\nOne-shot learning is used for classification tasks such as facial recognition, where the exact input and features must be transferred from one system to another.<\/li>  \r\n\r\n<li>Zero-shot learning<br>  \r\nThis strategy is used in cases such as machine translation where there is a lack of labeled data in the target language. This approach is used to deal with unseen data and would thus require additional data while training.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"applications\">Transfer learning applications<\/h2>\r\n<ul><li>Transfer learning with image data<br>\r\nA good example of how transfer learning works with deep learning can be demonstrated using predictive models that use image data as input. Developers can use existing image predictive models such as the Oxford VGG model, Google Inception Model or the Microsoft ResNet model and incorporate them into their target tasks. These pre-trained models have already extracted features from huge volumes of images and can be effectively used in a new model.<\/li> \r\n\r\n<li>Transfer learning with language data<br>  \r\nNatural language processing is another domain where transfer learning can find immense use. Similar word meanings can be easily derived by comparing the word mappings of a base model. This is done via word embedding, where certain words are mapped to a continuous vector space. Words with similar meanings will have similar vector space representations. This makes it easier for the newer models to form these word mappings with the knowledge transferred from a base model. There are quite a few efficient algorithms that make this transfer learning possible. Distributed word representation models such as Google\u2019s word2vec Model and Stanford\u2019s GloVe Model can be downloaded and used to develop deep learning language models faster and more efficiently.<\/li>  \r\n\r\n<li>Transfer learning from simulations<br> \r\nSimulations are used by many commercial ML applications to gather data and train models for further accuracy. The simulation presents a controlled and less risky way to safely gather data that can be used for further learning in machine learning systems. Udacity\u2019s open-source simulator and OpenAI\u2019s Universe are some simulation systems that serve as examples of this type of transfer learning application.<br> \r\n\r\nAs transfer learning can be effectively applied to image data, it has found great use in medical diagnosis with applications specializing in cancer subtype discovery, medical imaging, and so on. It is also used for general game playing, text classification, and digit recognition, all of which use image data as input. <br> \r\n\r\nTransfer learning also finds use in cognitive science and is being researched to be used in understanding EMG signals and EEG brainwaves. The use of transfer learning in CNNs and neural networks have proved to be more efficient as well.<\/li><\/ul>  \r\n\r\n<blockquote class=rounded><p class=\"pb-3\"><strong>Tip:<\/strong><\/p>\r\n<p class=\"pb-3\">Training Data is available from clickworker in all quantities and in high quality to train your transfer learning system optimally\r\n<\/p><a href=\"https:\/\/www.clickworker.com\/ai-datasets-for-machine-learning\/\" class=\"btn btn-primary\" role=\"button\">More About Datasets for Machine Learning<\/a><\/blockquote>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"advantages\">Advantages and challenges of transfer learning<\/h2> \r\n<p>Transfer learning can save up a lot of time that goes into training similar models. By applying the learnings from the first model, the second model training can be optimized and can also be able to have improved performance.  <br>\r\n\r\nHere are some of the advantages that transfer learning provides:<\/p>\r\n\r\n<ul><li>Helps with rapid training and progress of machine learning projects, given that a similar pilot project has already been used.<\/li>\r\n<li>Target models with transfer learning usually have a better-converged skill<\/li>\r\n<li>Allows you to create models even when you have little data available. As you must be aware, training machine learning models require a large amount of data. But in cases where data is scarce, using transfer learning could help develop models that would otherwise be impossible to develop.<\/li>\r\n<li>Improves the efficiency of reinforcement learning techniques.<\/li>\r\n<li>Helps reduce the computational power required to develop new models and train them.<\/li><\/ul>\r\n<p>In general, thus you use transfer learning when you are pressed for time and require better performance. Whenever you have an existing pre-trained model already in use for a similar task, it makes sense financially and logically to reuse it for better accuracy and faster time to market. Transfer learning also becomes much easier when both models have the same input.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"limitations\">Limitations of transfer learning<\/h2>\r\n<p>Having listed out the advantages of transfer learning, it is also necessary to note that it cannot be applied to every case. Transfer learning has its limitations, such as those explained below.<\/p>\r\n<ul><li>Transfer learning can only be utilized for generic features that can be easily compared between the two models. Unique and model-specific features cannot be replicated or found in other models; thus, transfer learning will find little use in such cases.<\/li>\r\n<li>You might have to take up certain preprocessing steps if the input data size for the pre-trained task A differs from that of the new task B.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"history\">History and future of transfer learning<\/h2>\r\n<p>The first mathematical model for transfer learning was put forth by Stevo Bozinovski and Ante Fulgosi in 1976. Later in 1981, the applicability of transfer learning was experimentally demonstrated. Several algorithms have since been published on transfer learning, specifically the 1993 discriminability-based transfer (DBT) algorithm. Works by Pratt and Sebastian Thrun have refined the transfer learning concept and advanced techniques such as multi-tasking learning. <br> \r\n\r\nA major motivation behind the AI-based transfer learning methodologies was seeded by The Neural Information Processing Systems (NIPS) 1995 workshop Learning to Learn: Knowledge Consolidation and Transfer in Inductive Systems.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"conclusion\">Conclusion<\/h2>\r\n<p>Transfer learning is one of the growing research topics among AI engineers and machine learning experts, given its capability to develop models quickly and efficiently. Andre Ng has famously noted that transfer learning would be the focus of commercial ML applications right after the more popular supervised learning.<br>  \r\n\r\nWhile they are still nowhere near to how actual knowledge transfer happens with human education, transfer learning presents a viable approach where AI systems can also learn from each other and grow more intelligent daily. It has found its use in many kinds of machine learning-based applications and is also applied in the field of cognitive science.<\/p>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"gray section-space\">\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"container\">\t\t\t\r\n\t\t<h2 id=\"faq\">FAQs on Transfer Learning<\/h2>\t\r\n\t\t<!--<div class=\"h2 d-block faq-snippet-heading cw-dark-gray\" data-toggle=\"collapse\" data-target=\"#faqCrowdsourcingSnippet\" role=\"button\" aria-expanded=\"false\" aria-controls=\"faqCrowdsourcingSnippet\">\r\n\t\t\t\tCrowdsourcing - Goolge FAQ Snippets\r\n\t\t\t<\/div>-->\r\n\t\t<div id=\"faqDataAnnotationSnippet\">\r\n\t\t\t\t<div class=\"row\">\r\n\t\t\t\t\t\t<div class=\"col-lg-12\">\r\n\t\t\t\t\t\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>When should transfer learning be used?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tTransfer learning can be used when you are pressed for time and require better performance. Whenever you have an existing pre-trained model already in use for a similar task, it makes sense financially and logically to reuse it for better accuracy and faster time to market.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What is the principle of transfer learning?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tTransfer learning is the process of using a machine learning model that has already been trained to solve a separate but connected problem.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What is the history of transfer learning<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\t<ul><li>The first mathematical model for transfer learning was put forth by Stevo Bozinovski and Ante Fulgosi in 1976.<\/li>\r\n\t\t\t\t\t\t\t\t<li>Later in 1981, the applicability of transfer learning was experimentally demonstrated.<\/li><\/ul>\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\n<script type=\"application\/ld+json\">\n\t{\n\t\t\"@context\": \"https:\/\/schema.org\",\n\t\t\"@type\": \"FAQPage\",\n\t\t\"mainEntity\": [\n\t\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"When should transfer learning be used?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Transfer learning can be used when you are pressed for time and require better performance. Whenever you have an existing pre-trained model already in use for a similar task, it makes sense financially and logically to reuse it for better accuracy and faster time to market.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What is the principle of transfer learning?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Transfer learning is the process of using a machine learning model that has already been trained to solve a separate but connected problem.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What is the history of transfer learning\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<ul><li>The first mathematical model for transfer learning was put forth by Stevo Bozinovski and Ante Fulgosi in 1976.<\/li><li>Later in 1981, the applicability of transfer learning was experimentally demonstrated.<\/li><\/ul>\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t\t\t\t]\n\t}\n<\/script>\n\r\n\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<\/div>\r\n\r\n\t<\/section>\r\n\r\n \r\n\r\n\r\n\r\n","protected":false},"excerpt":{"rendered":"<p>T Transfer Learning Transfer Learning\u2013 Short Explanation What is transfer learning? How does transfer learning work? Define Inductive transfer Transfer deep learning types Transfer learning applications Advantages and challenges of transfer learning Limitations of transfer learning History and future of transfer learning Conclusion FAQs Transfer Learning\u2013 Short Explanation Transfer learning is a machine learning phenomenon [&hellip;]<\/p>\n","protected":false},"author":65,"featured_media":0,"parent":54483,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"blank_with_breadcrumb.php","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Transfer Learning - AI glossary \/ term explanation<\/title>\n<meta name=\"description\" content=\"Transfer learning is a machine learning phenomenon where knowledge gained from solving one problem is applied to another similar problem.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.clickworker.com\/ai-glossary\/transfer-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Transfer Learning - 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