通过叠加任务特征实现模型融合，提升多任务性能。
Superpose Task-specific Features for Model Merging
- 基于线性表示假设，将各模型任务特征叠加融合。
- 在多个基准上优于现有方法，保持强多任务能力。
- 适合需要高效集成多个模型的场景。
模型融合可在不额外训练的情况下赋予神经网络强大能力。本文提出一种新视角：利用神经网络表征的基本机制进行模型融合。该方法基于线性表示假设——神经网络通过特征向量的线性组合编码信息。我们提出将各模型的任务特定特征叠加至合并模型中，特别针对深度网络中负责特征激活与提取的关键线性变换矩阵。通过将融合过程建模为线性系统，可有效保留各模型的任务特征，生成具备更强多任务能力的合并模型。在多种基准和模型上的大量实验表明，该方法显著优于现有技术。代码已公开于 https://github.com/LARS-research/STF。
原文摘要 · Abstract (English)
Model merging enables powerful capabilities in neural networks without requiring additional training. In this paper, we introduce a novel perspective on model merging by leveraging the fundamental mechanisms of neural network representation. Our approach is motivated by the linear representation hypothesis, which states that neural networks encode information through linear combinations of feature vectors. We propose a method that superposes task-specific features from individual models into a merged model. Our approach specifically targets linear transformation matrices, which are crucial for feature activation and extraction in deep networks. By formulating the merging process as a linear system, we can preserve task-specific features from individual models and create merged models that effectively maintain multi-task capabilities compared to existing methods. Extensive experiments across diverse benchmarks and models demonstrate that our method outperforms existing techniques. Code is available at https://github.com/LARS-research/STF.
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