Gradient-based class weighting for unsupervised domain adaptation in dense prediction visual tasks
In unsupervised domain adaptation (UDA), where models are trained on source data (e.g., synthetic) and adapted to target data (e.g., real-world) without target annotations, addressing the challenge of significant class imbalance remains an open issue. Despite progress in bridging the domain gap, exi...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universidad Autónoma de Madrid |
| Repository: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Language: | English |
| OAI Identifier: | oai:repositorio.uam.es:10486/719317 |
| Online Access: | http://hdl.handle.net/10486/719317 https://dx.doi.org/10.1016/j.patcog.2025.111633 |
| Access Level: | Open access |
| Keyword: | Class imbalance Domain adaptation Semantic segmentation Informática |
| Summary: | In unsupervised domain adaptation (UDA), where models are trained on source data (e.g., synthetic) and adapted to target data (e.g., real-world) without target annotations, addressing the challenge of significant class imbalance remains an open issue. Despite progress in bridging the domain gap, existing methods often experience performance degradation when confronted with highly imbalanced dense prediction visual tasks like semantic segmentation. This discrepancy becomes especially pronounced due to the lack of equivalent priors between the source and target domains, turning class imbalanced techniques used for other areas (e.g., image classification) ineffective in UDA scenarios. This paper proposes a class-imbalance mitigation strategy that incorporates class-weights into the UDA learning losses, with the novelty of estimating these weights dynamically through the gradients of the per-class losses, defining a Gradient-based class weighting (GBW) approach. The proposed GBW naturally increases the contribution of classes whose learning is hindered by highly-represented classes, and has the advantage of automatically adapting to training outcomes, avoiding explicit curricular learning patterns common in loss-weighing strategies. Extensive experimentation validates the effectiveness of GBW across architectures (Convolutional and Transformer), UDA strategies (adversarial, self-training and entropy minimization), tasks (semantic and panoptic segmentation), and datasets. Analysis shows that GBW consistently increases the recall of under-represented classes |
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