Deep model fusion/merging is an emerging technique that merges the parameters
or predictions of multiple deep learning models into a single one. It combines
the abilities of different models to make up for the biases and errors of a
single model to achieve better performance. However, deep model fusion on
large-scale deep learning models (e.g., LLMs and foundation models) faces
several challenges, including high computational cost, high-dimensional
parameter space, interference between different heterogeneous models, etc.
Although model fusion has attracted widespread attention due to its potential
to solve complex real-world tasks, there is still a lack of complete and
detailed survey research on this technique. Accordingly, in order to understand
the model fusion method better and promote its development, we present a
comprehensive survey to summarize the recent progress. Specifically, we
categorize existing deep model fusion methods as four-fold: (1) "Mode
connectivity", which connects the solutions in weight space via a path of
non-increasing loss, in order to obtain better initialization for model fusion;
(2) "Alignment" matches units between neural networks to create better
conditions for fusion; (3) "Weight average", a classical model fusion method,
averages the weights of multiple models to obtain more accurate results closer
to the optimal solution; (4) "Ensemble learning" combines the outputs of
diverse models, which is a foundational technique for improving the accuracy
and robustness of the final model. In addition, we analyze the challenges faced
by deep model fusion and propose possible research directions for model fusion
in the future. Our review is helpful in deeply understanding the correlation
between different model fusion methods and practical application methods, which
can enlighten the research in the field of deep model fusion.

深度模型融合是将多个深度学习模型的参数或预测合并为一个模型的新兴技术，以提高性能。研究对大规模深度学习模型的融合存在高计算成本、高维参数空间和不同异构模型之间的干扰等挑战。鉴于对该技术缺乏完整详细的调查研究，我们呈现了一份综合调查报告以总结最近的进展，并提出了未来模型融合的研究方向。该综述有助于深入了解不同模型融合方法与实际应用方法之间的关联，从而启发研究深度模型融合领域。