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[1] Arash Vahdat,et al. Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks , 2017, NIPS.
[2] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[3] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[4] Nojun Kwak,et al. Consistency-based Semi-supervised Learning for Object detection , 2019, NeurIPS.
[5] Simon Chadwick,et al. Training Object Detectors With Noisy Data , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).
[6] Vittorio Ferrari,et al. Revisiting Knowledge Transfer for Training Object Class Detectors , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Dumitru Erhan,et al. Training Deep Neural Networks on Noisy Labels with Bootstrapping , 2014, ICLR.
[8] Kai Chen,et al. MMDetection: Open MMLab Detection Toolbox and Benchmark , 2019, ArXiv.
[9] Yang Yang,et al. Learning to Localize Objects with Noisy Labeled Instances , 2019, AAAI.
[10] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Xiaogang Wang,et al. Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Frank Keller,et al. Training Object Class Detectors from Eye Tracking Data , 2014, ECCV.
[14] Kiyoharu Aizawa,et al. Joint Optimization Framework for Learning with Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Xingrui Yu,et al. How does Disagreement Help Generalization against Label Corruption? , 2019, ICML.
[16] Christoph H. Lampert,et al. Learning Intelligent Dialogs for Bounding Box Annotation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Geoffrey E. Hinton,et al. When Does Label Smoothing Help? , 2019, NeurIPS.
[18] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[19] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[20] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[21] Thomas Deselaers,et al. Localizing Objects While Learning Their Appearance , 2010, ECCV.
[22] Frank Keller,et al. Training Object Class Detectors with Click Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Kun Yi,et al. Probabilistic End-To-End Noise Correction for Learning With Noisy Labels , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] James Bailey,et al. Dimensionality-Driven Learning with Noisy Labels , 2018, ICML.
[25] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[26] Noel E. O'Connor,et al. Unsupervised label noise modeling and loss correction , 2019, ICML.
[27] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[28] Yuxing Tang,et al. Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Fei-Fei Li,et al. Best of both worlds: Human-machine collaboration for object annotation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[31] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Abhinav Gupta,et al. Learning from Noisy Large-Scale Datasets with Minimal Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Yuning Jiang,et al. Acquisition of Localization Confidence for Accurate Object Detection , 2018, ECCV.
[34] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[35] Cordelia Schmid,et al. Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Hao Su,et al. Crowdsourcing Annotations for Visual Object Detection , 2012, HCOMP@AAAI.
[37] Thomas G. Dietterich,et al. Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..
[38] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[39] Xingrui Yu,et al. Co-teaching: Robust training of deep neural networks with extremely noisy labels , 2018, NeurIPS.
[40] Trevor Darrell,et al. LSDA: Large Scale Detection through Adaptation , 2014, NIPS.
[41] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[42] Ramakant Nevatia,et al. NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[43] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[44] Lei Zhang,et al. CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] Li Fei-Fei,et al. MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels , 2017, ICML.
[46] Frank Keller,et al. We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[48] Pengfei Chen,et al. Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels , 2019, ICML.