Representation Surgery for Multi-Task Model Merging
Enneng Yang Li Shen Zhenyi Wang Guibing Guo Xiaojun Chen Xingwei Wang Dacheng Tao
Multi-task learning (MTL) compresses the information from multiple tasks into
a unified backbone to improve computational efficiency and generalization.
Recent work directly merges multiple independently trained models to perform
MTL instead of collecting their raw data for joint training, greatly expanding
the application scenarios of MTL. However, by visualizing the representation
distribution of existing model merging schemes, we find that the merged model
often suffers from the dilemma of representation bias. That is, there is a
significant discrepancy in the representation distribution between the merged
and individual models, resulting in poor performance of merged MTL. In this
paper, we propose a representation surgery solution called "Surgery" to reduce
representation bias in the merged model. Specifically, Surgery is a lightweight
task-specific module that takes the representation of the merged model as input
and attempts to output the biases contained in the representation from the
merged model. We then designed an unsupervised optimization objective that
updates the Surgery module by minimizing the distance between the merged
model's representation and the individual model's representation. Extensive
experiments demonstrate significant MTL performance improvements when our
Surgery module is applied to state-of-the-art (SOTA) model merging schemes.