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 is publicly available at github.com/cvi2snt/DDA-MLIC.

翻译：本文提出了一种无判别器的对抗式无监督域自适应（UDA）方法，用于多标签图像分类（MLIC），简称DDA-MLIC。近年来，已有研究尝试将对抗式UDA方法引入MLIC场景。然而，这些依赖额外判别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损害任务特定的判别能力。为此，本文提出通过引入一种直接源自任务特定分类器的新型对抗评判器来克服该问题。具体而言，我们利用双分量高斯混合模型（GMM）拟合源域与目标域的预测结果，从而区分两个聚类。该方法可提取每个分量对应的高斯分布，并基于弗雷歇距离构建对抗损失。通过在覆盖三种不同域偏移类型的多个多标签图像数据集上进行评估，结果表明DDA-MLIC在精度上优于现有最先进方法，且所需参数更少。相关代码已开源至github.com/cvi2snt/DDA-MLIC。