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Domain Randomization (DR) is known to require a significant amount of training data for good performance.
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Objectnet3d: A large scale database for 3d object recognition
Y. Xiang, W. Kim, W. Chen, J. Ji, C. Choy, H. Su, R. Mottaghi, L. Guibas, and S. Savarese · 2016
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Cycada: Cycle-consistent adversarial domain adaptation
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Synthesizing a scene-specific pedestrian detector and pose estimator for static video surveillance
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Domain randomization for scene-specific car detection and pose estimation
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Sharpening jensen’s inequality
J. Liao and A. Berg · 2018
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Syn2real: A new benchmark forsynthetic-to-real visual domain adaptation
X. Peng, B. Usman, K. Saito, N. Kaushik, J. Hoffman, and K. Saenko · 2018
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X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Structured domain randomization: Bridging the reality gap by context-aware synthetic data
A. Prakash, S. Boochoon, M. Brophy, D. Acuna, E. Cameracci, G. State, O. Shapira, and S. Birchfield · 2018
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Effective use of synthetic data for urban scene semantic segmentation
F. S. Saleh, M. S. Aliakbarian, M. Salzmann, L. Petersson, and J. M. Alvarez · 2018
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Implicit 3d orientation learning for 6d object detection from rgb images
M. Sundermeyer, Z. Marton, M. Durner, and R. Triebel · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke · 2018
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Y. Tassa, Y. Doron, A. Muldal, T. Erez, Y. Li, D. d. L. Casas, D. Budden, A. Abdolmaleki, J. Merel, A. Lefrancq, et al · 2018
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Training deep networks with synthetic data: Bridging the reality gap by domain randomization
J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield · 2018
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Deep visual domain adaptation: A survey
M. Wang and W. Deng · 2018
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