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Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world.
Contrastive adaptation network for unsupervised domain adaptation
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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P. Häusser, T. Frerix, A. Mordvintsev, and D. Cremers · 2017
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Y. Chen, W. Chen, Y. Chen, B. Tsai, Y. F. Wang, and M. Sun · 2017
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OpenAI, I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, J. Schneider, N. Tezak, J. Tworek, P. Welinder, L. Weng, Q. Yuan, W. Zaremba, and L. Zhang · 2019
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A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
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H. Zhao, R. T. des Combes, K. Zhang, and G. J. Gordon · 2019
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Dense Visual Learning for Robot Manipulation
P. Florence · 2019
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Dex-Net MM: Deep Grasping for Surface Decluttering with a Low-Precision Mobile Manipulator
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Domain adaptation with conditional distribution matching and generalized label shift, 2020
R. T. des Combes, H. Zhao, Y.-X. Wang, and G. Gordon · 2020
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