AI Research
Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits
Convolutional neural networks often contain redundant feature maps that increase storage and inference cost. This paper presents a loss-aware feature-map pruning framework using multi-armed bandits. Feature-map pruning is structured because it removes complete convolutional outpu...
AC
ArXiv CS.AI
Source
2 min
ArXiv CS.AI
AI Analysis
Generated insight by OnAirToday AI
This research introduces a novel, efficient method for pruning convolutional neural networks by treating feature maps as arms in a multi-armed bandit problem. The loss-aware approach intelligently identifies and removes redundant components, significantly reducing computational costs without sacrificing accuracy.
Powered by AI • Not a substitute for human analysisMore Insights →
Share
Filed under:AI Research
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.