论文标题
无线电发射器分类的快速蒙特卡洛辍学和误差校正
Fast Monte Carlo Dropout and Error Correction for Radio Transmitter Classification
论文作者
论文摘要
蒙特卡洛辍学物可以有效地捕获深度学习中的模型不确定性，在这种情况下，通过在测试时使用多个辍学实例获得了不确定性的度量。但是，蒙特卡洛辍学物在整个网络中都应用，因此显着提高了计算复杂性，与实例数量成正比。为了降低计算复杂性，在测试时，我们仅在神经网络的输出附近启用辍学层，并在保留其他辍学层时从先前的层中重复使用先前层的计算。此外，我们利用有关各种输入样本的理想分布的侧面信息来对预测进行“误差校正”。我们将这些技术应用于射频（RF）发射器分类问题，并表明所提出的算法能够提供比简单集合平均算法更好的预测不确定性，并且可以用于有效地识别在训练数据集中未正确分类的发射机，并且已经对其进行了培训。
Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of uncertainty is obtained by using multiple instances of dropout at test time. However, Monte Carlo dropout is applied across the whole network and thus significantly increases the computational complexity, proportional to the number of instances. To reduce the computational complexity, at test time we enable dropout layers only near the output of the neural network and reuse the computation from prior layers while keeping, if any, other dropout layers disabled. Additionally, we leverage the side information about the ideal distributions for various input samples to do `error correction' on the predictions. We apply these techniques to the radio frequency (RF) transmitter classification problem and show that the proposed algorithm is able to provide better prediction uncertainty than the simple ensemble average algorithm and can be used to effectively identify transmitters that are not in the training data set while correctly classifying transmitters it has been trained on.
