Structural pruning of neural networks conventionally relies on identifying and discarding less important neurons, a practice often resulting in significant accuracy loss that necessitates subsequent fine-tuning efforts. This paper introduces a novel approach named Intra-Fusion, challenging this prevailing pruning paradigm. Unlike existing methods that focus on designing meaningful neuron importance metrics, Intra-Fusion redefines the overlying pruning procedure. Through utilizing the concepts of model fusion and Optimal Transport, we leverage an agnostically given importance metric to arrive at a more effective sparse model representation. Notably, our approach achieves substantial accuracy recovery without the need for resource-intensive fine-tuning, making it an efficient and promising tool for neural network compression. Additionally, we explore how fusion can be added to the pruning process to significantly decrease the training time while maintaining competitive performance. We benchmark our results for various networks on commonly used datasets such as CIFAR-10, CIFAR-100, and ImageNet. More broadly, we hope that the proposed Intra-Fusion approach invigorates exploration into a fresh alternative to the predominant compression approaches. Our code is available here: https://github.com/alexandertheus/Intra-Fusion.

翻译：神经网络的结构化剪枝传统上依赖于识别并丢弃不重要的神经元，这种做法通常会导致显著的精度损失，进而需要后续的微调工作。本文介绍了一种名为Intra-Fusion的新方法，挑战了这一主流的剪枝范式。与现有方法专注于设计有意义的神经元重要性度量不同，Intra-Fusion重新定义了剪枝的底层流程。通过利用模型融合和最优传输的概念，我们借助一个无关给定重要性度量来获得更有效的稀疏模型表示。值得注意的是，我们的方法无需资源密集型的微调即可实现显著的精度恢复，使其成为一种高效且有前景的神经网络压缩工具。此外，我们探索了如何将融合加入剪枝过程，以在保持竞争性能的同时显著减少训练时间。我们在CIFAR-10、CIFAR-100和ImageNet等常用数据集上对各种网络进行了结果基准测试。更广泛地说，我们希望所提出的Intra-Fusion方法能激发对主流压缩方法全新替代方案的探索。我们的代码见：https://github.com/alexandertheus/Intra-Fusion。