Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks

Distilling the knowledge in a neural network

Original

Hinton, G., Vinyals, O., and Dean, J · 2015

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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., and Clune, J · 2015

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Rethinking the inception architecture for computer vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016

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Wide residual networks

Original

Zagoruyko, S. and Komodakis, N · 2016

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On calibration of modern neural networks

Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017

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A baseline for detecting misclassified and out-of-distribution examples in neural networks

Hendrycks, D. and Gimpel, K · 2017

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Densely connected convolutional networks

Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017

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Bag of tricks for image classification with convolutional neural networks

He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M · 2019

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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem

Hein, M., Andriushchenko, M., and Bitterwolf, J · 2019

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Deep anomaly detection with outlier exposure

Hendrycks, D., Mazeika, M., and Dietterich, T · 2019

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When does label smoothing help?

Müller, R., Kornblith, S., and Hinton, G. E · 2019

Later among the works it cites.

Self-training with noisy student improves imagenet classification

Original

Xie, Q., Hovy, E., Luong, M.-T., and Le, Q. V · 2019

Later among the works it cites.

Revisit knowledge distillation: a teacher-free framework

Original

Yuan, L., Tay, F. E., Li, G., Wang, T., and Feng, J · 2019

Later among the works it cites.

Does label smoothing mitigate label noise?

Original

Lukasik, M., Bhojanapalli, S., Menon, A. K., and Kumar, S · 2020

Closest in time.