Efficient Transfer Learning for Correlated Optimization Tasks with Reconstruction Loss

The paper proposes a novel transfer learning approach that explicitly encourages the learning of transferable features by introducing a reconstruction loss for common information shared across correlated optimization tasks. This approach enables efficient knowledge transfer and mitigates overfitting when training on limited target task data.