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f-Domain Adversarial Learning: Theory and Algorithms.

, , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 66-75. PMLR, (2021)

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David Méndez-Acuña

Rodrigo Acuna-Agost

Guillermo Acuña

Gustavo Alejandro Acuña Sanhueza

Luis Acuña Labraña

 

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