Performance improve techniques like AL, MTL, TL & FTW
These techniques are essential in modern machine learning and deep learning, offering ways to improve model performance, adaptability, and efficiency. Here’s an overview of how each technique works: 1. Active Learning (AL) Concept : Active learning is an approach where the model selectively queries a human (or other oracle) to label data points it finds most informative. Instead of training on a fixed dataset, the model identifies instances where additional information would be most beneficial. Process : The model begins with a small labeled dataset. During training, it identifies new, unlabeled instances that it struggles with, often those with high uncertainty or low confidence in predictions. These data points are then labeled by the oracle and added to the training set. Advantages : Reduces the amount of labeled data needed, which can be costly and time-consuming to obtain. This is especially useful in domains like medical imaging or natural language processing, where labels requir...