🤖 AI Summary
Existing domain adaptation methods (e.g., DSN) separate domain-invariant representations (DIReps) from domain-specific representations (DDReps) via orthogonality constraints. However, weak orthogonality often leaves discriminative information in DDReps—causing “information leakage” that degrades target-domain generalization. This paper proposes a KL-divergence-driven DDRep minimization mechanism that explicitly compresses the DDRep distribution within deep networks, thereby enhancing the purity and transferability of DIReps. The constraint is compatible with pretrained models and enables end-to-end domain-separation learning. Evaluated on standard image benchmarks (e.g., Office), the method achieves or surpasses state-of-the-art performance. Synthetic-data experiments demonstrate robustness to initialization and substantial improvements in cross-domain classification accuracy. The core innovation lies in replacing conventional orthogonality constraints with a distribution-level KL-divergence penalty, fundamentally mitigating representation leakage.
📝 Abstract
The most effective domain adaptation (DA) involves the decomposition of data representation into a domain independent representation (DIRep), and a domain dependent representation (DDRep). A classifier is trained by using the DIRep of the labeled source images. Since the DIRep is domain invariant, the classifier can be"transferred"to make predictions for the target domain with no (or few) labels. However, information useful for classification in the target domain can"hide"in the DDRep in current DA algorithms such as Domain-Separation-Networks (DSN). DSN's weak constraint to enforce orthogonality of DIRep and DDRep, allows this hiding and can result in poor performance. To address this shortcoming, we developed a new algorithm wherein a stronger constraint is imposed to minimize the DDRep by using a KL divergent loss for the DDRep in order to create the maximal DIRep that enhances transfer learning performance. By using synthetic data sets, we show explicitly that depending on initialization DSN with its weaker constraint can lead to sub-optimal solutions with poorer DA performance whereas our algorithm with maximal DIRep is robust against such perturbations. We demonstrate the equal-or-better performance of our approach against state-of-the-art algorithms by using several standard benchmark image datasets including Office. We further highlight the compatibility of our algorithm with pretrained models, extending its applicability and versatility in real-world scenarios.
