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Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels.
Adaptive background mixture models for real-time tracking
Stauffer, C. and Grimson, W. E. L · 1999
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Multi-armed Bandit Algorithms and Empirical Evaluation
Vermorel, J. and Mohri, M · 2005
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A study of Gaussian mixture models of color and texture features for image classification and segmentation
Permuter, H., Francos, J., and Jermyn, I · 2006
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L · 2009
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Bayesian Estimation of Beta Mixture Models with Variational Inference
Ma, Z. and Leijon, A · 2011
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Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2015
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Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
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Deep image homography estimation
DeTone, D., Malisiewicz, T., and Rabinovich, A · 2016
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Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
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Dense-Captioning Events in Videos
Krishna, R., Hata, K., Ren, F., Fei-Fei, L., and Niebles, J. C · 2017
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Learning Features by Watching Objects Move
Pathak, D., Girshick, R., Dollár, P., Darrell, T., and Hariharan, B · 2017
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Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L · 2017
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Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Vahdat, A · 2017
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Learning From Noisy Large-Scale Datasets With Minimal Supervision
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., and Belongie, S · 2017
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Understanding deep learning requires re-thinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
Guo, S., Huang, W., Zhang, H., Zhuang, C., Dong, D., Scott, M., and Huang, D · 2018
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Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K · 2018
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Liang, S., Li, Y., and Srikant, R · 2018
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Dimensionality-Driven Learning with Noisy Labels
Ma, X., Wang, Y., Houle, M., Zhou, S., Erfani, S., Xia, S.-T., Wijewickrema, S., and Bailey, J · 2018
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UMAP: uniform manifold approximation and projection
McInnes, L., Healy, J., Saul, N., and Großberger, L · 2018
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LF-Net: Learning Local Features from Images
Ono, Y., Trulls, E., Fua, P., and Moo Yi, K · 2018
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Pyramid Scene Parsing Network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J · 2017
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Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
Alvi, M., Zisserman, A., and Nellaker, C · 2018
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The Power of Ensembles for Active Learning in Image Classification
Beluch, W., Genewein, T., Nürnberger, A., and Köhler, J · 2018
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A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Ding, Y., Wang, L., Fan, D., and Gong, B · 2018
Cited alongside, same era.
Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Hyperspectral Image Classification in the Presence of Noisy Labels
Jiang, J., Ma, J., Wang, Z., Chen, C., and Liu, X
Cited in the paper.
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Learning to Reweight Examples for Robust Deep Learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
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Joint Optimization Framework for Learning with Noisy Labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
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A Light CNN for Deep Face Representation With Noisy Labels
Wu, X., He, R., Sun, Z., and Tan, T · 2018
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mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y., and Lopez-Paz, D · 2018
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On the Importance of Label Quality for Semantic Segmentation
Zlateski, A., Jaroensri, R., Sharma, P., and Durand, F · 2018
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