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STARFISH 方法提升剪枝神经网络的准确率恢复能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为 STARFISH 的新方法，可有效恢复剪枝后神经网络的准确率。剪枝可减小网络尺寸以加快推理速度，但通常会降低准确率。STARFISH 使用少量无标签样本，通过将剪枝网络的内部状态与其原始网络的表示对齐，来优化剪枝网络。该方法在恢复原始准确率方面显著优于现有方法，尤其是在激进剪枝的情况下。 AI

影响 提高了剪枝模型的效率，可能加速资源受限环境中神经网络的部署。

排序理由 该集群包含一篇详细介绍神经网络剪枝和准确率恢复新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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STARFISH 方法提升剪枝神经网络的准确率恢复能力

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该集群包含一篇详细介绍神经网络剪枝和准确率恢复新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shir Maon, Odelia Melamed, Adi Shamir ·

    STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing

    arXiv:2606.01126v1 Announce Type: cross Abstract: Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a considerable reduction in the model's accuracy, and thus it is usually followed b…

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