FusionBench: 一种统一的库和全面的深度模型融合基准
FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
AI总结：
FusionBench 是首个用于深度模型融合的统一库和基准，提供多种任务和设置以评估融合方法的有效性和鲁棒性。
摘要：
深度模型融合是一种新兴技术，通过以低成本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到一个性能更优的模型中。尽管已经介绍了多种深度模型融合技术，但它们的评估往往不一致，往往不足以验证其有效性和鲁棒性。我们提出了FusionBench，这是第一个用于深度模型融合的基准和统一库。我们的基准包含多个任务，每个任务有不同的模型和数据集设置。这种多样性使我们能够跨不同场景和模型规模比较融合方法。此外，FusionBench还为新融合技术的轻松实现和测试提供了统一的库。FusionBench是开源且持续维护的，鼓励社区贡献。主页 https://github.com/tanganke/fusion_bench
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
