Single Task Learning
Single Task Learning is a machine learning paradigm where a model is trained to perform one specific task, such as image classification or sentiment analysis, without being designed to handle multiple tasks simultaneously. It focuses on optimizing performance for that single objective, often leading to specialized and high-performing models for narrow applications. This approach contrasts with multi-task learning, where a single model learns to perform several related tasks at once.
Developers should use Single Task Learning when they need a model that excels at a specific, well-defined task, such as detecting spam emails or recognizing handwritten digits, as it typically achieves higher accuracy and simpler training compared to multi-task models. It is particularly useful in production environments where performance and reliability for a single function are critical, or when computational resources are limited and a lightweight, focused model is preferred.