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f-Domain Adversarial Learning: Theory and Algorithms.

, , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 66-75. PMLR, (2021)

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f-Domain Adversarial Learning: Theory and Algorithms., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 66-75. PMLR, (2021)Object Instance Annotation With Deep Extreme Level Set Evolution., , , , and . CVPR, page 7500-7508. Computer Vision Foundation / IEEE, (2019)Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting., , , , , , , and . CoRR, (2023)Efficient Interactive Annotation of Segmentation Datasets With Polygon-RNN++., , , and . CVPR, page 859-868. Computer Vision Foundation / IEEE Computer Society, (2018)Can Feedback Enhance Semantic Grounding in Large Vision-Language Models?, , , and . CoRR, (2024)Scalable Neural Data Server: A Data Recommender for Transfer Learning., , , and . NeurIPS, page 8984-8997. (2021)Domain Adversarial Training: A Game Perspective., , , and . ICLR, OpenReview.net, (2022)How Much More Data Do I Need? Estimating Requirements for Downstream Tasks., , , , , , , , and . CVPR, page 275-284. IEEE, (2022)Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data., , , , , , , and . ICRA, page 7249-7255. IEEE, (2019)Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting., , , , , , , and . Trans. Mach. Learn. Res., (2023)