Active learning is generally considered a subset of Machine Learning. It’s also sometimes referred to as a supervised form of machine learning. Here, the active learning system may interact with a human user to classify and label data for producing the required output. The ML algorithm chooses the next set of cases to classify from an unlabeled data set. The core idea behind active learner algorithms is to give the machine learning algorithm the freedom to select the data it wants to learn from. This helps improve its accuracy with fewer training labels.
Applications of Active Learning
Because it yields optimal performance with minimal labeled samples, Active Learning has broad practical applications in Artificial Intelligence. ML teams can save significant money by using it instead of standard Supervised Learning. Let’s investigate a few of them in more depth now.
1. Computer Vision
A wide variety of techniques for analyzing and improving visual content are included under the umbrella of Computer Vision. Because there is so much unlabelled data available on the internet, Active Learning is commonly used in this industry.
2. Image Classification
Image classification involves classifying the images under specific categories. Active Learning is used in the Cost-Effective Active Learning (CEAL) model for the classification of images, which proposes to automatically extract and pseudo-annotate unmarked samples rather than using the standard approach of only considering the most interesting, insightful, and representative samples.
3. Object Detection
Object detection is a technique utilized in computer vision that isolates things of interest in a given image. Object detection is not the same as image classification. For example, during image classification, an entire image is assigned to a single category, such as “busy road,” without any evidence to suggest otherwise.
4. Natural Language Processing
NLP, or natural language processing, is a subfield of AI that can understand spoken language in its most natural form (text in a majority of circumstances). This category encompasses a wide range of activities, such as text completion, emotion identification, etc. Due to its enormous success in Computer Vision, Active Learning has also become widely employed for many NLP tasks during the past decade.
5. Audio Processing
Spoken language identification (crucial in multilingual operations), audio synthesis, audio completion, etc., are all significant automated operations that fall under the umbrella of “audio processing.” Active learning can reduce human effort in supervising acoustic and language model training.
Active Learning is effective across multiple AI paradigms, including picture segmentation, scene identification, and other sectors like speech processing and NLP.
