Summary of Rl-pruner: Structured Pruning Using Reinforcement Learning For Cnn Compression and Acceleration, by Boyao Wang et al.
RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
by Boyao Wang, Volodymyr Kindratenko
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to compressing Convolutional Neural Networks (CNNs) using structured pruning. The authors observe that filters in different layers of a neural network have varying importance to the model’s performance and propose RL-Pruner, a reinforcement learning-based method to learn the optimal pruning distribution. The approach achieves a more compact architecture while maintaining target accuracy, ensuring compatibility with edge devices and reducing latency and computational costs. The paper demonstrates the effectiveness of RL-Pruner on models such as GoogleNet, ResNet, and MobileNet by comparing it to other structured pruning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to make Convolutional Neural Networks (CNNs) smaller and faster without losing their ability to perform well. The team found that some filters in the network are more important than others, and they developed a new way to remove less important filters while keeping the most important ones. This makes it possible to run the network on devices with limited resources, like smartphones or smart home appliances, and reduces the time it takes to make predictions. The researchers tested their approach on several well-known models and showed that it works well. |
Keywords
» Artificial intelligence » Neural network » Pruning » Reinforcement learning » Resnet
