We propose a novel way of solving the issue of classification of out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in the Generative Adversarial Network (GAN) framework. A generative model augments the data set in an online fashion with new samples and stochastic target vectors, while a discriminative model determines the class of the samples. The approach was evaluated on the UC2017 SG and UC2018 DualMyo data sets. The generative models performance was measured with a distance metric between generated and real samples. The discriminative models were evaluated by their accuracy on trained and novel classes. In terms of sample generation quality, the GAN is significantly better than a random distribution (noise) in mean distance, for all classes. In the classification tests, the baseline neural network was not capable of identifying untrained gestures. When the proposed methodology was implemented, we found that there is a trade-off between the detection of trained and untrained gestures, with some trained samples being mistaken as novelty. Nevertheless, a novelty detection accuracy of 95.4% or 90.2% (depending on the data set) was achieved with just 5% loss of accuracy on trained classes.

翻译：我们提出一种新颖的方法，利用在生成对抗网络（GAN）框架下训练的人工神经网络（ANN）来解决词汇外手势分类问题。生成模型以在线方式用新样本和随机目标向量扩充数据集，而判别模型则判定样本的类别。该方法在UC2017 SG和UC2018 DualMyo数据集上进行了评估。生成模型的性能通过生成样本与真实样本之间的距离度量来测量。判别模型则根据其在训练类别和新类别上的准确率进行评估。在样本生成质量方面，GAN在所有类别上的平均距离显著优于随机分布（噪声）。在分类测试中，基线神经网络无法识别未训练的手势。当实施所提出的方法时，我们发现训练手势与未训练手势的检测之间存在权衡，部分训练样本被误判为新奇样本。尽管如此，在仅损失5%训练类别准确率的情况下，新颖性检测准确率达到了95.4%或90.2%（取决于数据集）。