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Unsupervised transfer of object recognition models from synthetic to real data is an important problem with many potential applications.
Towards open world recognition
Bendale, A., Boult, T.: · 1902
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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 1920
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A database for handwritten text recognition research
Hull, J.J.: · 1994
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Columbia object image library (coil-20)
Nene, S.A., Nayar, S.K., Murase, H., et al.: · 1996
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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The cmu pose, illumination, and expression (pie) database
Sim, T., Baker, S., Bsat, M.: · 2002
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Cross-dataset action detection
Cao, L., Liu, Z., Huang, T.S.: · 2005
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Dataset Shift in Machine Learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: · 2009
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
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Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach
Bergamo, A., Torresani, L.: · 2010
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Visual event recognition in videos by learning from web data
Duan, L., Xu, D., Tsang, I., Luo, J.: · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., Bengio, Y.: · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: · 2011
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Unsupervised domain adaptation of virtual and real worlds for pedestrian detection
Vázquez, D., López, A.M., Ponsa, D.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Pedestrian path prediction with recursive bayesian filters: A comparative study
Schneider, N., Gavrila, D.M.: · 2013
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Selective transfer machine for personalized facial action unit detection
Chu, W.S., De la Torre, F., Cohn, J.F.: · 2013
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Equivalence of distance-based and rkhs-based statistics in hypothesis testing
Sejdinovic, D., Sriperumbudur, B., Gretton, A., Fukumizu, K.: · 2013
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Parsing IKEA Objects: Fine Pose Estimation
Lim, J.J., Pirsiavash, H., Torralba, A.: · 2013
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A testbed for cross-dataset analysis
Tommasi, T., Tuytelaars, T., Caputo, B.: · 2014
Cited alongside, same era.
Rendering synthetic ground truth images for eye tracker evaluation
Świrski, L., Dodgson, N.: · 2014
Cited alongside, same era.
Virtual and real world adaptation for pedestrian detection
Vazquez, D., Lopez, A.M., Marin, J., Ponsa, D., Geronimo, D.: · 2014
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2014
Cited alongside, same era.
Beyond pascal: A benchmark for 3d object detection in the wild
Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Playing for data: Ground truth from computer games
Richter, S.R., Vineet, V., Roth, S., Koltun, V.: · 2016
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SSD: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
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The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: · 2016
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The cityscapes dataset for semantic urban scene understanding
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Xiang, Y., Mottaghi, R., Savarese, S.: · 2014
Cited alongside, same era.
From virtual to reality: Fast adaptation of virtual object detectors to real domains
Sun, B., Saenko, K.: · 2014
Cited alongside, same era.
Microsoft COCO: common objects in context
Lin, T., Maire, M., Belongie, S.J., Bourdev, L.D., Girshick, R.B., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
Cited alongside, same era.
Multi-class open set recognition using probability of inclusion
Jain, L.P., Scheirer, W.J., Boult, T.E.: · 2014
Cited alongside, same era.
Beyond pascal: A benchmark for 3d object detection in the wild
Xiang, Y., Mottaghi, R., Savarese, S.: · 2014
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: · 2015
Cited alongside, same era.
Learning deep object detectors from 3d models
Peng, X., Sun, B., Ali, K., Saenko, K.: · 2015
Cited alongside, same era.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
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Objectnet3d: A large scale database for 3d object recognition
Xiang, Y., Kim, W., Chen, W., Ji, J., Choy, C., Su, H., Mottaghi, R., Guibas, L., Savarese, S.: · 2016
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2016
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Yolo9000: Better, faster, stronger
Redmon, J., Farhadi, A.: · 2016
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A large dataset of object scans
Choi, S., Zhou, Q.Y., Miller, S., Koltun, V.: · 2016
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Domain adaptation for visual applications: A comprehensive survey
Csurka, G.: · 2017
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Autodial: Automatic domain alignment layers
Carlucci, F.M., Porzi, L., Caputo, B., Ricci, E., Bulò, S.R.: · 2017
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CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: · 2017
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Open set domain adaptation
Busto, P.P., Gall, J.: · 2017
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Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video
Real, E., Shlens, J., Mazzocchi, S., Pan, X., Vanhoucke, V.: · 2017
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Self-ensembling for domain adaptation
French, G., Mackiewicz, M., Fisher, M.H.: · 2017
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Open set domain adaptation by backpropagation
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: · 2018
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