Domain adaptation is essential to enable wide usage of deep learning based
networks trained using large labeled datasets. Adversarial learning based
techniques have shown their utility towards solving this problem using a
discriminator that ensures source and target distributions are close. However,
here we suggest that rather than using a point estimate, it would be useful if
a distribution based discriminator could be used to bridge this gap. This could
be achieved using multiple classifiers or using traditional ensemble methods.
In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator
could suffice to obtain the distribution based discriminator. Specifically, we
propose a curriculum based dropout discriminator that gradually increases the
variance of the sample based distribution and the corresponding reverse
gradients are used to align the source and target feature representations. The
detailed results and thorough ablation analysis show that our model outperforms
state-of-the-art results.Comment: BMVC 2019 Accepted, Project Page:
https://delta-lab-iitk.github.io/CD3A
