  Multi-task learning, which optimizes performance across multiple tasks, is
inherently a multi-objective optimization problem. Various algorithms are
developed to provide discrete trade-off solutions on the Pareto front.
Recently, continuous Pareto front approximations using a linear combination of
base networks have emerged as a compelling strategy. However, it suffers from
scalability issues when the number of tasks is large. To address this issue, we
propose a novel approach that integrates a main network with several low-rank
matrices to efficiently learn the Pareto manifold. It significantly reduces the
number of parameters and facilitates the extraction of shared features. We also
introduce orthogonal regularization to further bolster performance. Extensive
experimental results demonstrate that the proposed approach outperforms
state-of-the-art baselines, especially on datasets with a large number of
tasks.
