How to Do Multi-Task Learning Intelligently
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How to Do Multi-Task Learning Intelligently
During the past decade, machine learning has exploded in popularity and is now being applied to problems in many fields. Traditionally, a single machine learning model is devoted to one task, e.g. classifying images, which is known as single-task learning (STL). There are some advantages, however, to training models to make multiple kinds of predictions on a single sample, e.g. image classification and semantic segmentation. This is known as Multi-task learning (MTL). In this article, we discuss the motivation for MTL as well as some use cases, difficulties, and recent algorithmic advances.
