Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property. To prevent the neural network from being undesirably exploited, non-transferable learning has been proposed to reduce the model generalization ability in specific target domains. However, existing approaches require labeled data for the target domain which can be difficult to obtain. Furthermore, they do not have the mechanism to still recover the model's ability to access the target domain. In this paper, we propose a novel unsupervised non-transferable learning method for the text classification task that does not require annotated target domain data. We further introduce a secret key component in our approach for recovering the access to the target domain, where we design both an explicit and an implicit method for doing so. Extensive experiments demonstrate the effectiveness of our approach.

翻译：训练一个良好的深度学习模型需要大量的数据和计算资源，这使得最终得到的神经模型成为一项宝贵的知识产权。为防止神经网络被不当利用，非迁移学习被提出以降低模型在特定目标域的泛化能力。然而，现有方法需要目标域的标注数据，而这可能难以获取。此外，这些方法缺乏在需要时恢复模型对目标域访问能力的机制。本文针对文本分类任务提出了一种新颖的无监督非迁移学习方法，该方法无需标注的目标域数据。我们进一步在方法中引入了一个密钥组件用于恢复对目标域的访问，并为此设计了显式和隐式两种实现方式。大量实验证明了我们方法的有效性。