Synergizing Intelligence: Enhancing Multi-task Learning Networks for Robust and Efficient AI Models

Abstract

Context: Multi-task Learning (MTL) Networks have emerged as a practical paradigm in machine learning, enabling the simultaneous learning of multiple tasks while leveraging shared knowledge, enhancing performance and efficiency.

Problem: Traditional single-task models often require large datasets and extensive computational resources and may need to generalize better across tasks. MTL Networks, by contrast, aim to overcome…