Deep Adversarial Attention Alignment for
Unsupervised Domain Adaptation:
the Benefit of Target Expectation Maximization
Abstract. In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN).
First, our approach transfers knowledge in all the convolutional layers
through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these
methods, however, the convolutional layers which underpin critical lowlevel domain knowledge cannot be updated directly towards reducing
domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based
on this assumption, we propose an attention alignment scheme on all
the target convolutional layers to uncover the knowledge shared by the
source domain. Second, we estimate the posterior label distribution of
the unlabeled data for target network training. Previous methods, which
iteratively update the pseudo labels by the target network and refine
the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution
to calculate the cross-entropy loss for training, thereby ameliorating the
error accumulation of the estimated labels. The two contributions allow
our approach to outperform the state-of-the-art methods by +2.6% on
the Office-31 dataset