[Paper Review] Cycle-consistent Conditional Adversarial Transfer Networks

Jingjing Li, Erpeng Chen+4 more|arXiv (Cornell University)|Sep 17, 2019

Domain Adaptation and Few-Shot Learning45 references4 citations

TL;DR

This paper proposes 3CATN, a cycle-consistent conditional adversarial transfer network that improves domain adaptation by introducing two feature translators and a cycle-consistent loss to ensure domain-invariant features can be reliably translated between domains. By addressing the instability of conditional adversarial training due to inaccurate classifier predictions, 3CATN achieves state-of-the-art performance on both classical and large-scale benchmarks, outperforming prior methods by significant margins.

ABSTRACT

Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions. Recently, adversarial training have been successfully applied to domain adaptation and achieved state-of-the-art performance. However, there is still a fatal weakness existing in current adversarial models which is raised from the equilibrium challenge of adversarial training. Specifically, although most of existing methods are able to confuse the domain discriminator, they cannot guarantee that the source domain and target domain are sufficiently similar. In this paper, we propose a novel approach named {\it cycle-consistent conditional adversarial transfer networks} (3CATN) to handle this issue. Our approach takes care of the domain alignment by leveraging adversarial training. Specifically, we condition the adversarial networks with the cross-covariance of learned features and classifier predictions to capture the multimodal structures of data distributions. However, since the classifier predictions are not certainty information, a strong condition with the predictions is risky when the predictions are not accurate. We, therefore, further propose that the truly domain-invariant features should be able to be translated from one domain to the other. To this end, we introduce two feature translation losses and one cycle-consistent loss into the conditional adversarial domain adaptation networks. Extensive experiments on both classical and large-scale datasets verify that our model is able to outperform previous state-of-the-arts with significant improvements.

Motivation & Objective

• To address the instability in conditional adversarial domain adaptation caused by inaccurate classifier predictions.
• To ensure that truly domain-invariant features can be reliably translated between source and target domains.
• To improve domain alignment by incorporating cycle-consistent feature translation constraints.
• To enhance the robustness and performance of adversarial domain adaptation beyond existing state-of-the-art methods.
• To validate the effectiveness of feature translators and cycle-consistent loss in reducing error propagation from unreliable classifier conditions.

Proposed method

• The method employs conditional adversarial training where the domain discriminator is conditioned on both learned features and classifier predictions to capture multimodal data structures.
• Two separate feature translators are introduced to map features from source to target and vice versa, enabling bidirectional translation of domain-invariant representations.
• A cycle-consistent loss is applied to enforce that translating a feature from source to target and back should reconstruct the original feature, ensuring consistency.
• The model is trained end-to-end with a combination of classification loss, conditional adversarial loss, feature translation losses, and cycle-consistent loss.
• Hyperparameters such as λ=1, β=1, η₁=0.01, and η₂=0.1 are used, with sensitivity analysis confirming stability across parameter variations.
• The framework is evaluated on multiple domain adaptation benchmarks including Office-Home, DomainNet, and VisDA-2017.

Experimental results

Research questions

• RQ1 Can feature translation between domains improve the reliability of conditional adversarial training in domain adaptation?
• RQ2 Does enforcing cycle consistency in feature translation lead to more stable and accurate domain-invariant representations?
• RQ3 How does the proposed method mitigate performance degradation caused by inaccurate classifier predictions in conditional domain adaptation?
• RQ4 To what extent do feature translators and cycle-consistent loss contribute to performance gains over existing state-of-the-art methods?
• RQ5 Is the proposed 3CATN framework robust to hyperparameter variations in real-world domain adaptation scenarios?

Key findings

• On the D→A and W→A benchmarks, 3CATN achieves 73.1% and 71.5% accuracy, respectively, significantly outperforming the previous SOTA method CDAN.
• The ablation study shows that adding feature translation loss improves performance by 0.9% on D→A and 1.2% on W→A compared to CDAN.
• The cycle-consistent loss contributes 1.2% accuracy gain on W→A, demonstrating its effectiveness in preserving feature integrity.
• In qualitative analysis, 3CATN correctly classifies 163 samples misclassified by CDAN on W→A, while maintaining high performance on all CDAN-correct samples.
• The model shows stable training behavior, achieving convergence within 10,000 iterations, and is robust to hyperparameter variations, especially β and η₁, η₂.
• The removal of the conditional transfer loss (S4) results in a drop to 67.5% and 66.9% on D→A and W→A, confirming the necessity of conditional supervision.