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AutoDFP: Automatic Data-Free Pruning Method for Neural Networks

Core Concepts
Automatic Data-Free Pruning (AutoDFP) method achieves pruning and reconstruction without fine-tuning, based on channel similarity.
The content discusses the AutoDFP method for automatic data-free pruning in neural networks. It introduces the problem of model compression and structured pruning methods, highlighting the limitations of data-dependent approaches. The AutoDFP method is proposed as an alternative that automates pruning and reconstruction based on channel similarity. The methodology involves reinforcement learning to optimize the pruning strategy for each layer. Experiments demonstrate significant improvements in accuracy compared to other methods across various datasets and network structures. Introduction to model compression and structured pruning methods. Proposal of AutoDFP for automatic data-free pruning. Methodology involving reinforcement learning for optimization. Experiment results showing improved accuracy compared to other methods.
AutoDFPは、VGG-16のCIFAR-10データセットで2.87%の精度損失削減を実現しました。 AutoDFPは、ImageNetデータセットでMobileNet-V1においてSOTA手法より43.17%高い精度を達成しました。 Neuron Mergingに比べ、ResNet-34では最大41.41%の精度向上が見られました。
"Most current structured pruning methods rely on training datasets to fine-tune the compressed model." "Some data-free methods have been proposed, however, these methods often require handcraft parameter tuning." "Our approach is based on the assumption that the loss of information can be partially compensated by retaining focused information from similar channels."

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by Siqi Li,Jun ... at 03-14-2024

Deeper Inquiries

How does AutoDFP compare to traditional fine-tuning methods in terms of efficiency


What are potential drawbacks or limitations of using reinforcement learning for automated pruning strategies


How can the concept of channel similarity in AutoDFP be applied to other areas outside of neural network pruning