1.0KaLabshttps://karthicklakshmanan.comkarthickhttps://karthicklakshmanan.com/index.php/author/karthick/rich600338<blockquote class="wp-embedded-content" data-secret="NSMjsDAKie"><a href="https://karthicklakshmanan.com/index.php/knowledge-base/transfer-learning/">Transfer Learning</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://karthicklakshmanan.com/index.php/knowledge-base/transfer-learning/embed/#?secret=NSMjsDAKie" width="600" height="338" title="“Transfer Learning” — KaLabs" data-secret="NSMjsDAKie" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); </script> Transfer Learning: Description: Transfer learning is a machine learning technique where knowledge gained from training a model on one task is applied to improve the performance on a different but related task. Instead of training a model from scratch for each specific task, transfer learning leverages prelearned features or representations obtained from a source task and adapts them to a target task. This approach is particularly useful when labeled data is limited for the target task. Key Components: Common Approaches: Use Cases: Challenges: Evaluation Metrics: Advancements and Trends: Applications: Transfer learning is a valuable technique, particularly in scenarios where labeled data for the target task is limited. It allows models to benefit from prelearned features and representations, speeding up training and improving performance on specific tasks.