CoSeP: Complementary Separability Pruning via Class-Separability Clustering
Researchers have developed a new neural network pruning technique called CoSeP, which aims to compress models more effectively. Unlike existing methods that score components independently, CoSeP considers the relationships between components by analyzing their class-separability profiles. This approach groups similar components and uses a knee-detection criterion to automatically determine the optimal number of components to retain, leading to significant reductions in computational cost and inference time without sacrificing accuracy. AI
IMPACT This method could lead to more efficient deployment of neural networks on resource-constrained devices.