1. Bibas, K., Feder, M., Hassner, T.: Single layer predictive normalized maximum likelihood for out-of-distribution detection. In: Advances in Neural Information Processing Systems, vol. 34, pp. 1179–1191 (2021)
2. Dhamija, A.R., Günther, M., Boult, T.: Reducing network agnostophobia. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
3. Dong, X., Guo, J., Li, A., Ting, W.T., Liu, C., Kung, H.: Neural mean discrepancy for efficient out-of-distribution detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19217–19227 (2022)
4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)
5. Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of International Conference on Learning Representations (2017)