A lightweight pruning framework with minimal retraining using Taylor expansion and multi-knowledge preservation strategy

Key Points

• The pruning framework preserves essential knowledge while minimizing retraining requirements, enhancing model efficiency.
• Efficient pruning reduces the computational load and maintenance costs associated with AI models.
• Analysis employs taylor expansion for optimizing performance without extensive retraining processes, improving overall adaptability.
• This method calls for more exploration of lightweight models for expansive AI applications, particularly in resource-constrained environments.