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Deep neural networks face storage and computational bottlenecks when deployed on resource-constrained devices. Structured pruning techniques can effectively achieve model compression and acceleration by removing redundant weights，but the adversarial robustness of traditional pruning networks is insufficient，limiting their application in security-sensitive scenarios. To balance the needs for model lightweighting and robustness enhancement，an iterative optimization method combining adversarial training and structured pruning was proposed：during the adversarial training process，the pruning mask is optimized synchronously，and an adaptive training-pruning frequency adjustment mechanism based on the “exploration-exploitation”strategy was innovatively designed to realize the dynamic optimization of hyperparameters. Experimental results on the CIFAR-10 dataset and ResNet-18 model show that，under a sparsity of 0.7，the proposed method increases the model's robust accuracy by 10.32%; in extreme scenarios where sparsity exceeds 0.9，the normal accuracy and robust accuracy are improved by 4.76% and 15.52% respectively; compared with the fixed-frequency strategy，the adaptive mechanism further enhances the normal accuracy by 0.80%～3.59% and the robust accuracy by 1.30%～8.50%，significantly reducing the cost of manual hyperparameter tuning. This research provides an effective technical solution for the secure and efficient deployment of deep neural networks on mobile platform.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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