  Domain adaptation (DA) aims at transferring knowledge from a labeled source
domain to an unlabeled target domain. Though many DA theories and algorithms
have been proposed, most of them are tailored into classification settings and
may fail in regression tasks, especially in the practical keypoint detection
task. To tackle this difficult but significant task, we present a method of
regressive domain adaptation (RegDA) for unsupervised keypoint detection.
Inspired by the latest theoretical work, we first utilize an adversarial
regressor to maximize the disparity on the target domain and train a feature
generator to minimize this disparity. However, due to the high dimension of the
output space, this regressor fails to detect samples that deviate from the
support of the source. To overcome this problem, we propose two important
ideas. First, based on our observation that the probability density of the
output space is sparse, we introduce a spatial probability distribution to
describe this sparsity and then use it to guide the learning of the adversarial
regressor. Second, to alleviate the optimization difficulty in the
high-dimensional space, we innovatively convert the minimax game in the
adversarial training to the minimization of two opposite goals. Extensive
experiments show that our method brings large improvement by 8% to 11% in terms
of PCK on different datasets.

Computer Vision and Pattern Recognition;Machine Learning