  Recently, deep self-training approaches emerged as a powerful solution to the
unsupervised domain adaptation. The self-training scheme involves iterative
processing of target data; it generates target pseudo labels and retrains the
network. However, since only the confident predictions are taken as pseudo
labels, existing self-training approaches inevitably produce sparse pseudo
labels in practice. We see this is critical because the resulting insufficient
training-signals lead to a suboptimal, error-prone model. In order to tackle
this problem, we propose a novel Two-phase Pseudo Label Densification
framework, referred to as TPLD. In the first phase, we use sliding window
voting to propagate the confident predictions, utilizing intrinsic
spatial-correlations in the images. In the second phase, we perform a
confidence-based easy-hard classification. For the easy samples, we now employ
their full pseudo labels. For the hard ones, we instead adopt adversarial
learning to enforce hard-to-easy feature alignment. To ease the training
process and avoid noisy predictions, we introduce the bootstrapping mechanism
to the original self-training loss. We show the proposed TPLD can be easily
integrated into existing self-training based approaches and improves the
performance significantly. Combined with the recently proposed CRST
self-training framework, we achieve new state-of-the-art results on two
standard UDA benchmarks.
