Domain Adaptation for Semantic Segmentation with Maximum Squares Loss (1909.13589v1)

Abstract: Deep neural networks for semantic segmentation always require a large number of samples with pixel-level labels, which becomes the major difficulty in their real-world applications. To reduce the labeling cost, unsupervised domain adaptation (UDA) approaches are proposed to transfer knowledge from labeled synthesized datasets to unlabeled real-world datasets. Recently, some semi-supervised learning methods have been applied to UDA and achieved state-of-the-art performance. One of the most popular approaches in semi-supervised learning is the entropy minimization method. However, when applying the entropy minimization to UDA for semantic segmentation, the gradient of the entropy is biased towards samples that are easy to transfer. To balance the gradient of well-classified target samples, we propose the maximum squares loss. Our maximum squares loss prevents the training process being dominated by easy-to-transfer samples in the target domain. Besides, we introduce the image-wise weighting ratio to alleviate the class imbalance in the unlabeled target domain. Both synthetic-to-real and cross-city adaptation experiments demonstrate the effectiveness of our proposed approach. The code is released at https://github. com/ZJULearning/MaxSquareLoss.

• The paper introduces the maximum squares loss to balance gradient distribution and enhance domain adaptation for semantic segmentation.
• It employs dynamic image-wise weighting to mitigate class imbalance in unlabeled target domains.
• Empirical results from synthetic-to-real and cross-city tests demonstrate superior performance over traditional entropy-based methods.

Domain Adaptation for Semantic Segmentation with Maximum Squares Loss

The paper presents an innovative approach to unsupervised domain adaptation (UDA) for semantic segmentation, focusing on addressing the intrinsic challenges posed by the need for large volumes of pixel-level annotations in deep learning frameworks. The key contributions of this work revolve around the proposed maximum squares loss and the implementation of an image-wise weighting ratio, aiming to effectively bridge the domain gap between synthesized labeled datasets and unlabeled real-world datasets.

Semantic segmentation tasks, particularly in real-world scenarios, face significant hurdles due to the exhaustive requirement of high-quality labels. The use of synthetic datasets like GTA5 and SYNTHIA is an attractive alternative, given their ease of annotation, but this introduces the challenge of domain shift due to visual dissimilarities between synthetic and real-world images. The authors propose a novel loss function, the maximum squares loss, to address this issue by mitigating the propensity of certain samples to dominate training due to skewed gradient tendencies in the entropy minimization approach typically used in semi-supervised learning methods.

The maximum squares loss aims to create a balanced training regimen by linearly increasing the gradient across samples, thereby ensuring that hard-to-transfer samples are not overlooked during training. This is a significant departure from conventional entropy-based methods that tend to concentrate gradient efforts on the easiest samples, as evidenced by the proposed gradient analyses and empirical validations provided by the authors.

Another notable innovation in this work is the image-wise weighting factor, designed to address class imbalances in the target domain that lack labels. This strategic approach outweighs the typical static class weighting paradigms by dynamically adjusting weights based on the distribution of predicted classes within individual images, enhancing the adaptation efficiency and boosting model performance across varying complexity classes.

Empirical results from synthetic-to-real and cross-city adaptation experiments substantiate the effectiveness of the proposed methodologies. The maximum squares loss achieved notable improvements over the baseline entropy minimization approach, demonstrating superior adaptation without the dependency on adversarial discriminators or additional network structures often required by other contemporary methods.

The inherent capability of the maximum squares loss in maximizing Pearson $\chi^2$ divergence supports class-wise distribution alignment, which is pivotal for achieving robust semantic coherence across domain shifts. Furthermore, the integration of multi-level outputs employing self-produced guidance bolsters the feature representation, further enhancing the segmentation accuracy and domain invariance of the models.

The implications of this research extend to the wider domain adaptation framework by presenting robust methodologies that can operate with reduced computational overhead while still delivering state-of-the-art results. It opens new avenues for practical applications of semantic segmentation in real-world systems, where annotation resources can often be limited. The future exploration of this work could involve extending the methodologies to more varied and complex domains, as well as investigating the combination of these mechanisms with other domain adaptation strategies for enhanced comprehensive performance.

In conclusion, the paper's contributions lie in the strategic formulation of the maximum squares loss and image-wise weighting to effectively tackle domain adaptation challenges, presenting a significant advancement in the field of semantic segmentation under unsupervised conditions.