mlsanityData to 7 Oct 2026

Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning

arXiv 2301.11063 ↗First posted 26 Jan 2023cs.CVcited by 0

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Abstract (arXiv)

Convolutional Neural Networks (CNNs) have a large number of parameters and take significantly large hardware resources to compute, so edge devices struggle to run high-level networks. This paper proposes a novel method to reduce the parameters and FLOPs for computational efficiency in deep learning models. We introduce accuracy and efficiency coefficients to control the trade-off between the accuracy of the network and its computing efficiency. The proposed Rewarded meta-pruning algorithm trains a network to generate weights for a pruned model chosen based on the approximate parameters of the final model by controlling the interactions using a reward function. The reward function allows more control over the metrics of the final pruned model. Extensive experiments demonstrate superior performances of the proposed method over the state-of-the-art methods in pruning ResNet-50, MobileNetV1, and MobileNetV2 networks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 53 of its 72 references that are in the map. First posted on arXiv 26 Jan 2023.

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References · 53

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38 more
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