Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
@article{Tjio2021AdversarialSH, title={Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation}, author={Gabriel Tjio and Ping Liu and Joey Tianyi Zhou and Rick Siow Mong Goh}, journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, year={2021}, pages={3849-3858} }
Convolutional neural networks typically perform poorly when the test (target domain) and training (source domain) data have significantly different distributions. While this problem can be mitigated by using the target domain data to align the source and target domain feature representations, the target domain data may be unavailable due to privacy concerns. Consequently, there is a need for methods that generalize well despite restricted access to target domain data during training. In this…
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