论文标题
自我注意力盗窃检测
Electricity Theft Detection with self-attention
论文作者
论文摘要
在这项工作中，我们提出了一个新型的自我注意机制模型，以解决不平衡现实数据集中的电力盗窃检测，该数据集呈现出中国州立电网公司提供的每日电力消耗。我们的关键贡献是引入了与扩张卷积相连的多头自我发项机制，并由内核尺寸$ 1 $统一的统一。此外，我们引入了一个二进制输入通道（二进制掩码），以识别缺失值的位置，从而使网络可以学习如何处理这些值。我们的模型达到了0.926美元的AUC，相对于以前的基准工作，它的提高超过$ 17 \％。该代码可在https://github.com/neuralmind-ai/electricity-theft-detection-with-self-oneverention上在GitHub上获得。
In this work we propose a novel self-attention mechanism model to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China. Our key contribution is the introduction of a multi-head self-attention mechanism concatenated with dilated convolutions and unified by a convolution of kernel size $1$. Moreover, we introduce a binary input channel (Binary Mask) to identify the position of the missing values, allowing the network to learn how to deal with these values. Our model achieves an AUC of $0.926$ which is an improvement in more than $17\%$ with respect to previous baseline work. The code is available on GitHub at https://github.com/neuralmind-ai/electricity-theft-detection-with-self-attention.
