相关实验视频

Updated: Jul 24, 2026

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators
11:44

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators

Published on: August 16, 2014

缓解低频偏差:特征重新校准和频率注意力规范化,以实现对抗性强度.

Kejia Zhang1, Juanjuan Weng2, Yuanzheng Cai3

  • 1Department of Artificial Intelligence, Xiamen University, Xiamen, 361005, Fujian, China.

Neural networks : the official journal of the International Neural Network Society
|September 11, 2025
PubMed
概括

对深层神经网络的对抗性训练会产生低频偏差. 我们的高频特征解和重新校准 (HFDR) 方法通过重新校准频率特征来提高稳定性.

关键词:
对抗性的训练是对抗性的训练.频率 频率 是一个频率.神经网络的稳定性 神经网络的稳定性

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