In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Recently, some attempts have been made for introducing adversarial-based UDA methods in the context of MLIC. However, these methods which rely on an additional discriminator subnet present one major shortcoming. The learning of domain-invariant features may harm their task-specific discriminative power, since the classification and discrimination tasks are decoupled. Herein, we propose to overcome this issue by introducing a novel adversarial critic that is directly deduced from the task-specific classifier. Specifically, a two-component Gaussian Mixture Model (GMM) is fitted on the source and target predictions in order to distinguish between two clusters. This allows extracting a Gaussian distribution for each component. The resulting Gaussian distributions are then used for formulating an adversarial loss based on a Frechet distance. The proposed method is evaluated on several multi-label image datasets covering three different types of domain shift. The obtained results demonstrate that DDA-MLIC outperforms existing state-of-the-art methods in terms of precision while requiring a lower number of parameters. The code will be made publicly available online.

翻译：本文提出了一种无需判别器的对抗性无监督域适应（UDA）方法，用于多标签图像分类（MLIC），称为DDA-MLIC。近年来，已有一些尝试将基于对抗的UDA方法引入多标签图像分类领域。然而，这些依赖额外判别器子网络的方法存在一个主要缺陷：由于分类任务与判别任务相互解耦，学习域不变特征可能会损害其任务特定的判别能力。为此，我们提出通过引入一种直接源于任务特定分类器的新型对抗评估器来克服该问题。具体地，在源域和目标域预测结果上拟合一个双分量高斯混合模型（GMM），以区分两个聚类，从而为每个分量提取一个高斯分布。随后，基于弗雷歇距离利用这些高斯分布构建对抗损失。所提方法在覆盖三种不同域漂移类型的多个多标签图像数据集上进行了评估。结果表明，DDA-MLIC在精度上优于现有最先进方法，同时所需参数更少。代码将公开发布。