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We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.
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ImageNet Large Scale Visual Recognition Challenge
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
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Scalable, high-quality object detection
C. Szegedy, S. Reed, D. Erhan, D. Anguelov, and S. Ioffe · 2014
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Object detection via a multi-region & semantic segmentation-aware cnn model
S. Gidaris and N. Komodakis · 2015
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Fast R-CNN
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Unifying distillation and privileged information
D. Lopez-Paz, L. Bottou, B. Schölkopf, and V. Vapnik · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Temporal ensembling for semi-supervised learning
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Feature pyramid networks for object detection
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Faster R-CNN: Towards real-time object detection with region proposal networks
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Hand keypoint detection in single images using multiview bootstrapping
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Revisiting unreasonable effectiveness of data in deep learning era
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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