  Many existing approaches for unsupervised domain adaptation (UDA) focus on
adapting under only data distribution shift and offer limited success under
additional cross-domain label distribution shift. Recent work based on
self-training using target pseudo-labels has shown promise, but on challenging
shifts pseudo-labels may be highly unreliable, and using them for self-training
may cause error accumulation and domain misalignment. We propose Selective
Entropy Optimization via Committee Consistency (SENTRY), a UDA algorithm that
judges the reliability of a target instance based on its predictive consistency
under a committee of random image transformations. Our algorithm then
selectively minimizes predictive entropy to increase confidence on highly
consistent target instances, while maximizing predictive entropy to reduce
confidence on highly inconsistent ones. In combination with pseudo-label based
approximate target class balancing, our approach leads to significant
improvements over the state-of-the-art on 27/31 domain shifts from standard UDA
benchmarks as well as benchmarks designed to stress-test adaptation under label
distribution shift.
