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Gradually Vanishing Bridge for Adversarial Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, Qi Tian
TL;DR
Unsupervised domain adaptation must learn domain-invariant representations despite rich domain-specific characteristics and domain discrepancy that is difficult to minimize directly. The paper introduces Gradually Vanishing Bridge on the generator and discriminator, and reports stronger adaptation across three challenging datasets, including state-of-the-art performance on Office-Home and improvements to other adversarial methods.
Problem
Rich domain-specific characteristics make domain-invariant representation learning difficult, while direct discrepancy minimization is hard and explicit decomposition can leave residual domain-specific characteristics.
Method
The paper equips adversarial domain adaptation with Gradually Vanishing Bridge on both generator and discriminator to reduce transfer difficulty, suppress residual domain characteristics, and balance adversarial training.
Results
GVB-GD outperforms competitors on three challenging datasets, achieves state-of-the-art on Office-Home, and improves existing adversarial methods including CDAN and Symnets.
Takeaways & Limitations
GVB provides a generally applicable mechanism for adversarial domain adaptation by combining more domain-invariant representations with balanced adversarial training.
Abstract
from arXiv · showhide
In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Some methods alleviate the difficulty by explicitly modeling domain-invariant and domain-specific parts in the representations, but the adverse influence of the explicit construction lies in the residual domain-specific characteristics in the constructed domain-invariant representations. In this paper, we equip adversarial domain adaptation with Gradually Vanishing Bridge (GVB) mechanism on both generator and discriminator. On the generator, GVB could not only reduce the overall transfer difficulty, but also reduce the influence of the residual domain-specific characteristics in domain-invariant representations. On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process. Experiments on three challenging datasets show that our GVB methods outperform strong competitors, and cooperate well with other adversarial methods. The code is available at https://github.com/cuishuhao/GVB.
1. Introduction
Unsupervised domain adaptation must transfer knowledge from labeled source data to unlabeled target data despite rich domain-specific characteristics and difficult-to-eliminate discrepancy. The paper proposes Gradually Vanishing Bridge on both generator and discriminator to ease transfer, reduce residual domain characteristics, and balance adversarial training.
- Unsupervised domain adaptation transfers knowledge from labeled source data to related unlabeled target data, where collecting target labels is expensive and time-consuming.
- Existing methods often directly minimize domain discrepancy, while explicit domain-specific/domain-invariant modeling can leave residual domain-specific characteristics in intermediate representations.
- GVB defines a bridge as a measurement of discrepancy between existing and ideal representations, applied to both generator and discriminator.
- On the generator, GVB progressively reduces the bridge range to alleviate transfer difficulty, reduce domain-specific characteristics, and limit hard-example influence.
- GVB-GD integrates generator and discriminator bridges to balance the two-player minmax game, outperform competitors on three challenging datasets, and achieve state-of-the-art on Office-Home.
- Visualization further indicates that larger GVB-G output ranges tend to correspond to higher misclassification probability.
2. Related Work
Prior domain-adaptation work uses moment alignment or adversarial training, while bridge-based methods construct intermediate representations through manifolds or image reconstruction. The paper instead targets a balanced adversarial framework with bridges on both generator and discriminator.
- Deep visual domain-adaptation methods mainly use moment alignment or adversarial training to address source-target discrepancy.
- Adversarial approaches such as DANN, ADDA, CyCADA, SBADA, MCD, CDAN, and Symnets build frameworks that confuse or condition domain classifiers.
- Earlier bridge methods use Grassmannian manifolds or pixel-level image reconstruction, but reconstruction can leave residual domain-specific characteristics and produce a large bridge range.
- GVB builds a bridge on both generator and discriminator to reduce negative domain-specific influence while providing a more balanced minmax adversarial game.
3. Method
The method inserts bridges into adversarial domain adaptation to ease transfer through an intermediate domain and progressively suppress domain-specific residuals. GVB is applied to both generator and discriminator to improve representation quality and balance the minmax game.
- 3.2. Gradually Vanishing Bridge on Generator: GVB constructs an intermediate representation by subtracting a learned domain-specific bridge γi from classifier response ci.The bridge is γi = G3(G1(xi)), and ri = ci − γi.
- 3.2. Gradually Vanishing Bridge on Generator: Large bridge ranges indicate hard examples and poorly separated domain-specific and domain-invariant components, increasing target-domain misclassification risk.Existing reconstruction-based bridges can retain substantial domain-specific characteristics and affect both ci and ri.
- 3.2. Gradually Vanishing Bridge on Generator: GVB progressively minimizes the overall range of γi, encouraging bridge values toward zero and reducing their negative influence on ci and ri.The resulting mechanism is called GVB-G when applied to the generator.
- 3.2. Gradually Vanishing Bridge on Generator: In the toy generator view, minimizing |γ| moves source and target distributions closer to the intermediate domain, while outside points remain hard examples or noisy data.The candidate intermediate domain expands to cover more points during adaptation.
- 3.3. Gradually Vanishing Bridge on Discriminator: GVB-D adds a discriminator bridge and progressively minimizes its range so the discriminator matches the stronger generator and supports a more balanced adversarial game.The discriminator bridge is σi = D2(G⋆(xi)).
- 3.3. Gradually Vanishing Bridge on Discriminator: GVB-G and GVB-D are combined as GVB-GD, and the mechanism is also applied to CDAN and SymNets according to each method's architecture.Both bridges can be applied to CDAN, whereas SymNets receives only GVB-G because it lacks an explicit discriminator.
4. Experiment
Experiments across Office-31, Office-Home, and VisDA-2017 evaluate GVB under varied domain discrepancy and dataset scale. Results support its effectiveness, visualization explains its bridge behavior, and ablations show the generator and discriminator bridges complement each other.
- Results: GVB also improves existing adversarial methods, with results for CDAN-G, CDAN-D, CDAN-GD, and Symnets-G supporting broad applicability.The reported improvements indicate cooperation with multiple adversarial domain adaptation frameworks.
- Results: GVB methods remain effective across easy and difficult domain shifts and across relatively small and large datasets.Office-Home and VisDA-2017 have larger domain discrepancies than Office, while VisDA-2017 is substantially larger than the Office datasets.
- Qualitative Validation: Increasing bridge amplification produces generated images that gradually transition between source and target domain characteristics, validating that γ captures rich domain-specific representations.The visualization varies γ amplification while decoding representations derived from classifier responses.
- Qualitative Validation: Larger generator bridge ranges correspond to higher classification error, whereas discriminator behavior assigns larger |σ_i| to more domain-invariant representations.Samples with larger |γ_i| contain more domain-specific properties, smaller |σ_i|, and higher misclassification probability.
- Ablation Study: Ablations show that progressive bridge minimization is essential on the generator, and GVB-D cooperates with GVB-G better than an unminimized discriminator bridge.GVB-G outperforms B-G by a large margin, while GVB-GD outperforms the combination of GVB-G and B-D.
- Qualitative Validation: t-SNE visualizations place GVB-GD target samples closer to source examples and produce more compact clusters than ResNet50 and the baseline.The baseline achieves global alignment but retains a large average distance between target data and nearest source data.
5. Conclusion
The paper concludes that Gradually Vanishing Bridge can support adversarial domain adaptation on both generator and discriminator sides. The combined mechanism improves representations, balances training, and transfers effectively to existing methods.
- GVB on the generator promotes more domain-invariant representations and reduces the negative influence of rich domain-specific characteristics.
- GVB on the discriminator provides additional discriminative power to achieve a more balanced adversarial training process.
- Applying GVB to existing methods produces substantial improvements over their original counterparts.