Iterative Pruning

Iterative pruning is a technique for compressing deep neural networks by repeatedly removing less important weights or connections during training, aiming to minimize model size and computational cost while preserving accuracy. Current research focuses on improving pruning algorithms, particularly for large language models and vision transformers, exploring various criteria for identifying weights to remove (e.g., magnitude, activation values, attention maps) and optimizing training schedules to mitigate performance degradation. This work is significant because it addresses the growing need for efficient deployment of large models on resource-constrained devices, impacting both the efficiency of AI applications and the broader understanding of network architecture and training dynamics.