Curriculum based Dropout Discriminator for Domain Adaptation
arXiv:1907.10628
Abstract
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.
arXiv commentBMVC 2019 Accepted, Project Page: https://delta-lab-iitk.github.io/CD3A/
References in corpus (11)
- Learning Transferable Features with Deep Adaptation Networks
- Deep Domain Confusion: Maximizing for Domain Invariance
- Conditional Adversarial Domain Adaptation
- Unsupervised Domain Adaptation with Residual Transfer Networks
- Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
- Beyond Sharing Weights for Deep Domain Adaptation
- Wasserstein Distance Guided Representation Learning for Domain Adaptation
- Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
- Adversarial Dropout Regularization
- Bayesian GAN
- Improved Training with Curriculum GANs