- The paper introduces Transferrable Prototypical Networks (TPN) for unsupervised domain adaptation by unifying general-purpose and task-specific adaptation to align class prototypes and sample-level score distributions.
- Experimental results on Digits and VisDA 2017 datasets show TPN outperforms state-of-the-art methods, achieving 94.1% accuracy on U
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- TPN provides a robust framework by integrating class-level and sample-level alignments, offering a practical advantage as it does not require retraining for new target examples.
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
The paper presents an innovative approach for unsupervised domain adaptation through the reformulation of Prototypical Networks, referred to as Transferrable Prototypical Networks (TPN). The core contribution of this work is the unification of general-purpose adaptation and task-specific adaptation within Prototypical Networks to mitigate the domain gap observed when transferring knowledge from a labeled source domain to an unlabeled target domain.
The existing challenges in unsupervised domain adaptation are predominantly centered around the discrepancies in data distributions across different domains, which if not addressed, can significantly impair classification performance. TPN approaches this problem by learning an embedding space where each class prototype from the source domain and its counterpart from the target domain are aligned. This alignment is achieved by implementing two main adaptation mechanisms in TPN: general-purpose domain adaptation and task-specific domain adaptation.
General-purpose Domain Adaptation
General-purpose adaptation in TPN is facilitated by aligning prototypes across domains. Each target example is assigned a "pseudo" label based on its nearest source domain prototype. Prototypes are then computed separately for source-only, target-only (with pseudo labels), and source-target data combined. The class-level discrepancy is measured by the RKHS distance between the prototypes across domains, forging a finer-grained alignment of data distributions compared to existing approaches that typically employ metric learning strategies such as Maximum Mean Discrepancy (MMD).
Task-specific Domain Adaptation
Task-specific adaptation is introduced to further align the classifiers' outputs by bridging the sample-level domain discrepancies. Specifically, TPN enforces similar score distributions over class predictions across different domains for each sample, utilizing KL-divergence to capture and minimize the mismatch of score distributions. The adaptation thus endeavors to ensure that classification decisions made by prototypes in each domain remain consistently accurate, bringing a significant advantage over purely feature-focused unsupervised adaptation methods.
Experimental Results
Extensive experimentation on Digits datasets (MNIST, USPS, SVHN) and the VisDA 2017 challenge dataset shows that TPN outperforms state-of-the-art domain adaptation methods, including those based on adversarial training and MMD. Notable improvements are reported, particularly for transfers involving substantial domain shifts, underscoring the efficacy of the dual adaptation approach in TPN.
On Digits datasets, TPN achieves remarkable accuracy boosts over baseline approaches. For instance, the method attains 94.1% on the U → M task, outperforming the next best competitor ADDA, which achieves 90.1%. On the more challenging VisDA 2017 dataset, TPN achieves an accuracy of 80.4%, demonstrating its strong capability in managing synthetic-to-real domain shifts.
Implications and Future Directions
TPN provides a robust framework for unsupervised domain adaptation by integrating class-level and sample-level alignments in a joint optimization scheme, potentially encouraging broader application in scenarios with high inter-domain variance. The architecture's flexibility in classifying new target examples without retraining specific domain models offers a practical advantage in dynamic environments.
The proposed TPN opens avenues for further theoretical exploration and optimization in aligning domain-invariant embeddings. Future research directions could explore adaptive mechanisms within TPN, leveraging fuzzy pseudo-labeling to dynamically adjust the influence of pseudo-label assigned samples in the learning process. Furthermore, extending the framework to incorporate temporal adaptations, particularly in continuously evolving domains, could enhance its applicability.
In conclusion, TPN presents a significant advancement in unsupervised domain adaptation methodologies, demonstrating the potential of embedding-based prototypical alignment in crafting effective domain adaptation frameworks. The introduction of adaptable embeddings that accommodate the unique features of both the source and target domains paves the way for more resilient and generalized model architectures.