Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and data-efficient manner. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness. We present FusionBench, the first benchmark and a unified library designed specifically for deep model fusion. Our benchmark consists of multiple tasks, each with different settings of models and datasets. This variety allows us to compare fusion methods across different scenarios and model scales. Additionally, FusionBench serves as a unified library for easy implementation and testing of new fusion techniques. FusionBench is open source and actively maintained, with community contributions encouraged. Homepage https://github.com/tanganke/fusion_bench

翻译：深度模型融合是一种新兴技术，旨在以经济高效和数据高效的方式，将多个深度神经网络的预测或参数统一为单个性能更优的模型。尽管已有多种深度模型融合技术被提出，但其评估往往不一致，且通常不足以验证其有效性和鲁棒性。我们提出了FusionBench，这是首个专为深度模型融合设计的基准测试及统一库。我们的基准包含多个任务，每个任务具有不同的模型和数据集设置。这种多样性使我们能够在不同场景和模型规模下比较融合方法。此外，FusionBench作为一个统一库，便于新融合技术的实现与测试。FusionBench是开源项目并持续维护，鼓励社区贡献。项目主页：https://github.com/tanganke/fusion_bench