The paper presents significant advancements in neural network pruning by utilizing a graph representation of neural architectures. This novel framework replaces outdated local optimization strategies with a global perspective, fostering improved network compression techniques. By employing a Graph Attention Network encoder, the method is capable of creating detailed embeddings that significantly enhance the agent’s ability to make pruning decisions.
In addition to its innovative architecture, the research addresses the limitations of existing pruning methods, allowing for the transition from broad continuous pruning towards more precise binary action spaces. This novel approach enables learning from data-driven criteria rather than relying on static scoring functions. Results from extensive experiments conducted on datasets like CIFAR-10, CIFAR-100, and ImageNet demonstrate that this method surpasses traditional pruning techniques, achieving state-of-the-art performance and identifying unnecessary connections more effectively than previous methods.
👉 Pročitaj original: arXiv AI Papers
