This paper proposes a novel adversarial scheme for learning from data under harsh learning conditions of partially labelled samples and skewed class distributions. This novel scheme integrates the generative ability of the state-of-the-art conditional generative adversarial network with the semi-supervised deep ladder network and semi-supervised deep auto-encoder. The proposed generative-adversarial based semi-supervised learning framework, named GBSS, is a triple network that aims to optimize a newly defined objective function to enhance the performance of the semi-supervised learner with the help of a generator and discriminator. The duel between the generator and discriminator results in the generation of more synthetic minority class samples that are very similar to the original minority samples (attacks and faults). Meanwhile, GBSS trains the semi-supervised model to learn the general distribution of the minority class samples including the newly generated samples in contrast to other classes and iteratively adjusts its weights. Moreover, a diagnostic framework is designed, in which GBSS and several state-of-the-art semi-supervised learners are used for learning and diagnosing attacks and faults in power grids. These methods are evaluated and compared for diagnosing attacks and faults in two different power grid cases. The attained results demonstrate the superiority of GBSS in diagnosing attacks and faults under the harsh conditions.

中文翻译：

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用于诊断电网故障和攻击的对抗性半监督学习

本文提出了一种新的对抗方案，用于在部分标记样本和偏态类分布的严酷学习条件下从数据中学习。这种新颖的方案将最先进的条件生成对抗网络的生成能力与半监督深度阶梯网络和半监督深度自动编码器相结合。提出的基于生成对抗的半监督学习框架，名为 GBSS，是一个三重网络，旨在优化新定义的目标函数，以在生成器和鉴别器的帮助下提高半监督学习器的性能。生成器和鉴别器之间的决斗导致生成更多合成的少数类样本，这些样本与原始少数类样本（攻击和故障）非常相似。同时，GBSS 训练半监督模型来学习少数类样本的一般分布，包括与其他类相比的新生成的样本，并迭代调整其权重。此外，还设计了一个诊断框架，其中 GBSS 和几个最先进的半监督学习器用于学习和诊断电网中的攻击和故障。对这些方法进行了评估和比较，以诊断两种不同电网情况下的攻击和故障。获得的结果证明了GBSS在恶劣条件下诊断攻击和故障方面的优越性。其中 GBSS 和几个最先进的半监督学习器用于学习和诊断电网中的攻击和故障。对这些方法进行了评估和比较，以诊断两种不同电网情况下的攻击和故障。获得的结果证明了GBSS在恶劣条件下诊断攻击和故障方面的优越性。其中 GBSS 和几个最先进的半监督学习器用于学习和诊断电网中的攻击和故障。对这些方法进行了评估和比较，以诊断两种不同电网情况下的攻击和故障。获得的结果证明了GBSS在恶劣条件下诊断攻击和故障方面的优越性。