Distinction Maximization Loss: Efficiently Improving Out-of-Distribution Detection and Uncertainty Estimation by Replacing the Loss and Calibrating

A limited memory algorithm for bound constrained optimization

Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C · 1995

Earlier work this paper cites.

Algorithm 778: L-BFGS-B: fortran subroutines for large-scale bound-constrained optimization

Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J · 1997

Earlier work this paper cites.

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F · 2009

Earlier work this paper cites.

Learning multiple layers of features from tiny images

Krizhevsky, A · 2009

Earlier work this paper cites.

Remark on "algorithm 778: L-BFGS-B: fortran subroutines for large-scale bound constrained optimization"

Morales, J. L. and Nocedal, J · 2011

Earlier work this paper cites.

Reading digits in natural images with unsupervised feature learning

Netzer, Y. and Wang, T · 2011

Earlier work this paper cites.

Obtaining well calibrated probabilities using bayesian binning

Naeini, M. P., Cooper, G. F., and Hauskrecht, M · 2015

Earlier work this paper cites.

LSUN: construction of a large-scale image dataset using deep learning with humans in the loop

Original

Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015

Earlier work this paper cites.

Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., and Sun, J · 2016

Earlier work this paper cites.

Large-margin softmax loss for convolutional neural networks

Liu, W., Wen, Y., Yu, Z., and Yang, M · 2016

Earlier work this paper cites.

Wide residual networks

Zagoruyko, S. and Komodakis, N · 2016

Earlier work this paper cites.

On calibration of modern neural networks

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

Cited alongside, same era.

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

Hendrycks, D. and Gimpel, K · 2017

Cited alongside, same era.

Densely connected convolutional networks

Huang, G., Liu, Z., Maaten, L. v. d., and Weinberger, K. Q · 2017

Cited alongside, same era.

Simple and scalable predictive uncertainty estimation using deep ensembles

Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017

Cited alongside, same era.

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 · 2018

Cited alongside, same era.

A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Yolov4: Optimal speed and accuracy of object detection

Original

Bochkovskiy, A., Wang, C., and Liao, H. M · 2020

Later among the works it cites.

Generalized ODIN: Detecting out-of-distribution image without learning from out-of-distribution data

Hsu, Y.-C., Shen, Y., Jin, H., and Kira, Z · 2020

Later among the works it cites.

Simple and principled uncertainty estimation with deterministic deep learning via distance awareness

Liu, J. Z., Lin, Z., Padhy, S., Tran, D., Bedrax-Weiss, T., and Lakshminarayanan, B · 2020

Later among the works it cites.

Hyperparameter-free out-of-distribution detection using cosine similarity

Techapanurak, E., Suganuma, M., and Okatani, T · 2020

Later among the works it cites.

Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network

van Amersfoort, J. R., Smith, L., Teh, Y. W., and Gal, Y · 2020

Later among the works it cites.