Multi-Task Cooperative Learning via Searching for Flat Minima
Fuping Wu Le Zhang Yang Sun Yuanhan Mo Thomas Nichols Bartlomiej W. Papiez
Multi-task learning (MTL) has shown great potential in medical image
analysis, improving the generalizability of the learned features and the
performance in individual tasks. However, most of the work on MTL focuses on
either architecture design or gradient manipulation, while in both scenarios,
features are learned in a competitive manner. In this work, we propose to
formulate MTL as a multi/bi-level optimization problem, and therefore force
features to learn from each task in a cooperative approach. Specifically, we
update the sub-model for each task alternatively taking advantage of the
learned sub-models of the other tasks. To alleviate the negative transfer
problem during the optimization, we search for flat minima for the current
objective function with regard to features from other tasks. To demonstrate the
effectiveness of the proposed approach, we validate our method on three
publicly available datasets. The proposed method shows the advantage of
cooperative learning, and yields promising results when compared with the
state-of-the-art MTL approaches. The code will be available online.