Bi-Directional Generation for Unsupervised Domain Adaptation
This addresses domain adaptation for visual tasks, but appears incremental as it builds on existing methods with novel generators and classifiers.
The paper tackles unsupervised domain adaptation by proposing a Bi-Directional Generation model that balances domain gap reduction and data structure preservation, achieving state-of-the-art performance on standard cross-domain visual benchmarks.
Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classifiers and the cross-domain alignment constraints. We also design two classifiers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks.