Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation (1809.09478v3)

Abstract: We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. A popular strategy is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the local category-level feature distribution. A possible consequence of the global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped. To address this problem, this paper introduces a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level data distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.

An Expert Overview of "Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation"

The paper "Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation" by Luo et al. addresses the challenge of unsupervised domain adaptation (UDA) in semantic segmentation. The primary focus is on overcoming the limitations of traditional adversarial learning methods that align feature distributions at a global level. These methods often neglect category-level semantic consistency, leading to misaligned features and degraded segmentation accuracy in the target domain.

Key Contributions

The paper introduces a Category-level Adversarial Network (CLAN) that prioritizes local semantic consistency while achieving global feature alignment. CLAN leverages adaptive adversarial loss weighting based on category-level alignment, thus reducing negative transfer effects where well-aligned features may be incorrectly mapped across domains.

Theoretical Framework

CLAN utilizes a co-training approach to determine the semantic alignment of features across domains. Two classifiers provide diverse perspectives on feature alignment, enabling CLAN to adjust the influence of adversarial loss dynamically. Specifically, CLAN decreases the adversarial loss on features that are well-aligned and increases the loss on poorly aligned features. This adaptive mechanism is crucial for maintaining category-level consistency during global alignment.

Methodology

The network architecture consists of a feature extractor, two classifiers providing distinct views, and a domain discriminator. The ensemble prediction from the classifiers undergoes adversarial training, with the discrepancy between the classifiers’ predictions serving as an indicator to adjust the adversarial weights. This dual-strategy fosters an environment where features are aligned both globally and at the category-level, enhancing semantic consistency.

Experimental Evaluations

The research evaluates CLAN on two primary domain adaptation tasks: GTA5 to Cityscapes and SYNTHIA to Cityscapes. In both cases, the CLAN framework demonstrates notable improvements over baseline models and other state-of-the-art methods, particularly in handling infrequent classes that are typically susceptible to negative transfer. On the GTA5 to Cityscapes task, CLAN achieved a significant increase in mean Intersection over Union (mIoU), illustrating its efficacy in preserving local alignment during global distribution adjustments.

Numerical Results

• GTA5 to Cityscapes (VGG-16 backbone): CLAN achieved an mIoU of 36.6%, marking an improvement over traditional adversarial methods by 1.6%.
• SYNTHIA to Cityscapes (ResNet-101 backbone): CLAN recorded an mIoU of 47.8%, demonstrating a notable enhancement over pre-existing models.

Implications and Future Directions

The implications of this work are multifaceted. Practically, CLAN can be applied to various domain adaptation tasks where semantic segmentation is critical, such as autonomous driving and medical image analysis. Theoretically, this approach opens avenues for further research into adaptive adversarial techniques and the integration of co-training methods within domain adaptation frameworks.

The future of AI in domain adaptation may involve exploring more sophisticated methods for determining semantic alignment, potentially incorporating additional context or leveraging self-supervised learning techniques to further refine category-level consistency across domains.

In summary, the paper makes significant strides in addressing the pitfalls of traditional adversarial learning in domain adaptation by introducing an innovative category-level approach that emphasizes semantic consistency, achieving competitive performance and advancing the state of research in the field.