Curriculum based Dropout Discriminator for Domain Adaptation
This work addresses the problem of domain adaptation for deep learning models, enabling better generalization from labeled source to unlabeled target domains, but it appears incremental as it builds on existing adversarial learning techniques.
The paper tackles domain adaptation by proposing a curriculum-based dropout discriminator that uses Monte Carlo dropout to create a distribution-based discriminator, gradually increasing variance to align source and target feature representations, and it reports outperforming state-of-the-art results.
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.