A Hybrid Filter Pruning Method Based on Linear Region Analysis

This study proposes a hybrid filter pruning method based on linear region analysis. Our approach combines the advantages of cluster pruning and norm-based filter pruning by introducing thresholds based on Euclidean distance and norm distance. We also incorporate the linear region analysis approach in neural network architecture search to estimate the performance of trained model architectures. This enables us to efficiently search for the optimal pruned structure with corresponding thresholds for effective model compression.