  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.
