Multi-task learning is a technique in which the network is trained to solve several problems concurrently. It is somewhat conceptually similar to transfer learning.
For example, the network might take an image and simultaneously learn to segment the scene, estimate the pixel-wise depth, and predict a caption describing the image. All of these tasks require some understanding of the image and, when learned simultaneously, the model performance for each may improve.
