Enhanced Structured Lasso Pruning with Class-wise Information

- The study presents innovative pruning techniques for neural networks that leverage class-wise information to maintain statistical integrity during model optimization. This approach addresses limitations in existing methods that may compromise performance by neglecting important data relationships. The introduction of sGLP-IB and sTLP-IB is significant as it enhances model efficiency while achieving notable parameter reductions and accuracy retention across various datasets.