CWPS: Efficient Channel-Wise Parameter Sharing for Knowledge Transfer.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Knowledge transfer is crucial for applying existing knowledge to new tasks and data.
• Current methods like fine-tuning offer coarse-grained solutions, limiting efficiency and performance.
• Identifying fine-grained objects for knowledge sharing remains a challenge.

Purpose of the Study:

• To propose a novel fine-grained parameter-sharing method for efficient knowledge transfer.
• To address the limitations of coarse-grained sharing in current methods.
• To enhance the performance and efficiency of knowledge transfer in machine learning models.

Main Methods:

• Channel-Wise Parameter Sharing (CWPS) refines granularity from layers to neurons for fine-grained sharing.
• An effective search strategy is employed to minimize computational costs and simplify weight selection.
• CWPS is designed to be comprehensive, plug-and-play, and composable.

Main Results:

• CWPS achieves state-of-the-art precision-to-parameter ratio performance across various backbones.
• Demonstrated efficiency and versatility in both Incremental Learning and Multi-Task Learning scenarios.
• The method shows strong composability and generalization capabilities.

Conclusions:

• CWPS offers a significant advancement in fine-grained parameter sharing for knowledge transfer.
• The proposed method enhances both performance and computational efficiency.
• CWPS is theoretically applicable to networks with linear and convolution layers.