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Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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The Filter Bubble: What the Internet Is Hiding from You
E. Pariser · 2011
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Human action recognition by learning bases of action attributes and parts
B. Yao, X. Jiang, A. Khosla, A. L. Lin, L. J. Guibas, and F.-F. Li · 2011
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Deep speech: Scaling up end-to-end speech recognition
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, et al · 2014
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Fast r-cnn
R. Girshick · 2015
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Deep learning face attributes in the wild
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Image style transfer using convolutional neural networks
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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"why should i trust you?" explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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M. Yatskar, L. Zettlemoyer, and A. Farhadi · 2016
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D. Arpit, S. Jastrzȩbski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, et al · 2017
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Unlearn dataset bias in natural language inference by fitting the residual
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Benchmarking neural network robustness to common corruptions and perturbations
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Learning not to learn: Training deep neural networks with biased data
B. Kim, H. Kim, K. Kim, S. Kim, and J. Kim · 2019
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Robust inference via generative classifiers for handling noisy labels
K. Lee, S. Yun, K. Lee, H. Lee, B. Li, and J. Shin · 2019
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Repair: Removing representation bias by dataset resampling
Y. Li and N. Vasconcelos · 2019
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J. Choi, C. Gao, J. C. Messou, and J.-B. Huang · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
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Learning robust representations by projecting superficial statistics out
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Learning de-biased representations with biased representations
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
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