Adversarial training has been instrumental in advancing multi-domain text classification (MDTC). Traditionally, MDTC methods employ a shared-private paradigm, with a shared feature extractor for domain-invariant knowledge and individual private feature extractors for domain-specific knowledge. Despite achieving state-of-the-art results, these methods grapple with the escalating model parameters due to the continuous addition of new domains. To address this challenge, we introduce the Stochastic Adversarial Network (SAN), which innovatively models the parameters of the domain-specific feature extractor as a multivariate Gaussian distribution, as opposed to a traditional weight vector. This design allows for the generation of numerous domain-specific feature extractors without a substantial increase in model parameters, maintaining the model's size on par with that of a single domain-specific extractor. Furthermore, our approach integrates domain label smoothing and robust pseudo-label regularization to fortify the stability of adversarial training and to refine feature discriminability, respectively. The performance of our SAN, evaluated on two leading MDTC benchmarks, demonstrates its competitive edge against the current state-of-the-art methodologies. The code is available at https://github.com/wangxu0820/SAN.

介绍了Stochastic Adversarial Network (SAN)，通过引入多元高斯分布模型领域特定特征提取器的参数，与传统的权重向量不同，可生成许多领域特定特征提取器而不增加模型参数，同时结合域标签平滑和健壮的伪标签正则化以提高对抗训练的稳定性和特征区分能力，在两个主要的多领域文本分类基准测试上表现出竞争优势。

多领域文本分类的随机对抗网络