Typical deep visual recognition models are capable of performing the one task
they were trained on. In this paper, we tackle the extremely difficult problem
of combining completely distinct models with different initializations, each
solving a separate task, into one multi-task model without any additional
training. Prior work in model merging permutes one model to the space of the
other then adds them together. While this works for models trained on the same
task, we find that this fails to account for the differences in models trained
on disjoint tasks. Thus, we introduce "ZipIt!", a general method for merging
two arbitrary models of the same architecture that incorporates two simple
strategies. First, in order to account for features that aren't shared between
models, we expand the model merging problem to additionally allow for merging
features within each model by defining a general "zip" operation. Second, we
add support for partially zipping the models up until a specified layer,
naturally creating a multi-head model. We find that these two changes combined
account for a staggering 20-60% improvement over prior work, making the merging
of models trained on disjoint tasks feasible.

本文提出了 “ZipIt！” 方法，通过特征合并和部分合并层实现两个架构相同的模型的合并，使得合并不同领域训练的模型变得更为可行。