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: this https URL

该论文介绍了一种名为 Intra-Fusion 的新方法，通过使用模型融合和最优传输的概念，重新定义了神经网络修剪的程序，实现了更有效的稀疏模型表示，并在不需要资源密集型微调的情况下显著提高了准确性恢复，从而成为神经网络压缩的一种高效且有前景的工具。