HASTE框架实现无需训练的CNN压缩

报道来源 [3]

1. arXiv cs.AI TIER_1 English(EN) · Miko{\l}aj Janusz, Tomasz Wojnar, Yawei Li, Luca Benini, Kamil Adamczewski · 2026-07-01 04:00

   One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression

   arXiv:2508.13836v2 Announce Type: replace-cross Abstract: Pruning is a core technique for compressing neural networks to improve computational efficiency. This process is typically approached in two ways: one-shot pruning, which involves a single pass of training and pruning, and…

2. arXiv cs.CV TIER_1 English(EN) · Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache · 2026-06-30 04:00

   HASTE：一种用于预训练卷积神经网络的无训练、动态和可控压缩框架

   arXiv:2606.30516v1 Announce Type: new Abstract: Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic execution methods are promising, existing approaches for CNNs typically require …

3. arXiv cs.CV TIER_1 English(EN) · Alexandru Paul Condurache · 2026-06-29 16:24

   HASTE：一种用于预训练卷积神经网络的无训练、动态和可控压缩框架

   Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic execution methods are promising, existing approaches for CNNs typically require specialized training or fine-tuning, limiting th…