English(EN) VLCQ: Post-training quantization for deep neural networks using variable length coding

VLCQ方法利用变长编码增强深度神经网络压缩

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一种新颖的深度神经网络后训练量化方法VLCQ，旨在提高资源受限设备的效率。与通常依赖INT8定点整数且在较低比特宽度下性能下降的现有方法不同，VLCQ利用变长编码实现更大的压缩。实验表明，VLCQ在图像分类、分割和目标检测等各种任务中，可以在保持或提高性能的同时，超越FP32精度和其他最先进的方法，提供显著的压缩比。 AI

影响 VLCQ为模型压缩提供了一种新方法，有望在边缘设备上实现更高效的深度学习模型部署。

排序理由该集群包含一篇详细介绍深度神经网络量化新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Reem Abdel-Salam, Ahmed H. Abdel-Gawad, Amr G. Wassal · 2026-10-15 04:00

VLCQ：使用变长编码对深度神经网络进行训练后量化

arXiv:2610.15092v1 Announce Type: cross Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not require access to the original dataset or retraining the full model. Current meth…

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报道来源 [1]

VLCQ：使用变长编码对深度神经网络进行训练后量化

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