Curriculum based Dropout Discriminator for Domain Adaptation

Understand

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.