An Effective Levelling Paradigm for Unlabeled Scenarios
Fangming Cui, Di Yang, Yuqiang Ren, Zhou Yu, Liang Xiao, Xinmei Tian
Advancements in directly-integration parameter optimization have underscored their potential to enhance the performance of labeled scenarios and tasks. One inherent flaw of these methods is that the optimized parameters usually exhibit weak performance on unlabeled tasks or scenarios. This may be attributed to the fact that the uncoordinated learning of directly-integration framework. To mitigate this issue of uncoordinated learning, we propose a novel method called Levelling Paradigm (LePa) to improve performance for unlabeled tasks or scenarios.The proposed LePa dynamically constrains and coordinates multiple objective functions,  thereby improving the robustness of coordinated fine-tuning. Comprehensive experiments demonstrate that the LePa outperforms existing methods.