TABLE 1. Notations.

Symbols	Description
${\mathcal{D}}^s$	set of labeled samples in the source domain
${\mathcal{D}}^t$	set of unlabeled samples in the target domain
${\mathcal{D}}^{t^{\text{'}}}$	set of labeled samples in the target domain
${\mathcal{L}}^s$	set of labels for the source domain
${\mathcal{L}}^t$	set of labels for the target domain
${\mathcal{L}}^c$	set of common labels across domains
${\overline{\mathcal{L}}}^s$	set of domain-specific labels in the source domain
${\overline{\mathcal{L}}}^t$	set of domain-specific labels in the target domain
$\mathcal{L}$	set of all labels from all domains
$N^s$	number of labeled samples in the source domain
$N^t$	number of unlabeled samples in the target domain
$N^{t^{\text{'}}}$	number of labeled samples in the target domain
$G_f$	feature extractor
$G_y$	multi-label classifier for $\mathcal{L}$
$G_{y_l}$	binary classifier for label $l$ (part of $G_y$)
$R$	common label recognizer
$D_c$	domain discriminator for common labels ${\mathcal{L}}^c$
$D_g$	general domain discriminator
$L_{G_y}$	loss of multi-label classification over the entire dataset
$L_R$	loss of $R$ over the entire dataset
$L_{D_g}$	loss of $D_g$ over the entire dataset
$L_{D_c}$	loss of $D_c$ over the entire dataset
$\lambda$	the coefficient of losses
$\mathbf{x}$	input image
$\mathbf{h}$	hidden features
$y$	ground-truth label
$\widehat{y}$	predicted probability
$\widehat{d}$	predicted probability that $\mathbf{x}$ belongs to source domain
$\widehat{r}$	predicted probability that $\mathbf{x}$ has common labels