Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
This addresses the challenge of efficient and generalizable network pruning for deep learning practitioners, offering a novel approach that avoids hand-crafted or training-intensive methods.
The paper tackles the problem of network pruning by proposing a meta-learning framework using graph metanetworks to automatically learn pruning strategies, achieving state-of-the-art results on various pruning tasks for CNNs and Transformers without requiring special training during pruning.
We propose an entirely new meta-learning framework for network pruning. It is a general framework that can be theoretically applied to almost all types of networks with all kinds of pruning and has great generality and transferability. Experiments have shown that it can achieve outstanding results on many popular and representative pruning tasks (including both CNNs and Transformers). Unlike all prior works that either rely on fixed, hand-crafted criteria to prune in a coarse manner, or employ learning to prune ways that require special training during each pruning and lack generality. Our framework can learn complex pruning rules automatically via a neural network (metanetwork) and has great generality that can prune without any special training. More specifically, we introduce the newly developed idea of metanetwork from meta-learning into pruning. A metanetwork is a network that takes another network as input and produces a modified network as output. In this paper, we first establish a bijective mapping between neural networks and graphs, and then employ a graph neural network as our metanetwork. We train a metanetwork that learns the pruning strategy automatically and can transform a network that is hard to prune into another network that is much easier to prune. Once the metanetwork is trained, our pruning needs nothing more than a feedforward through the metanetwork and some standard finetuning to prune at state-of-the-art. Our code is available at https://github.com/Yewei-Liu/MetaPruning.