Self-adaptive Re-weighted Adversarial Domain Adaptation
arXiv:2006.00223
Abstract
Existing adversarial domain adaptation methods mainly consider the marginal distribution and these methods may lead to either under transfer or negative transfer. To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution. In order to promote positive transfer and combat negative transfer, we reduce the weight of the adversarial loss for aligned features while increasing the adversarial force for those poorly aligned measured by the conditional entropy. Additionally, triplet loss leveraging source samples and pseudo-labeled target samples is employed on the confusing domain. Such metric loss ensures the distance of the intra-class sample pairs closer than the inter-class pairs to achieve the class-level alignment. In this way, the high accurate pseudolabeled target samples and semantic alignment can be captured simultaneously in the co-training process. Our method achieved low joint error of the ideal source and target hypothesis. The expected target error can then be upper bounded following Ben-David's theorem. Empirical evidence demonstrates that the proposed model outperforms state of the arts on standard domain adaptation datasets.
arXiv commentto appear in IJCAI2020
References in corpus (10)
- Learning Transferable Features with Deep Adaptation Networks
- Deep Domain Confusion: Maximizing for Domain Invariance
- Conditional Adversarial Domain Adaptation
- Domain Separation Networks
- Asymmetric Tri-training for Unsupervised Domain Adaptation
- Adversarial Discriminative Domain Adaptation
- Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
- Domain-Symmetric Networks for Adversarial Domain Adaptation
- Partial Adversarial Domain Adaptation
- Domain Alignment with Triplets