Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algorithms assume that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect outof-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes. Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classification tasks. Additionally, we experimentally show that the combination of our method with the Mahalanobis distance-based classifier achieves state-of-the-art results in the OOD detection task.
This paper proposes to tackle the problems of out-of-distribution (OOD) detection and model calibration by adapting the loss function of the Outlier Exposure (OE) technique [1]. In OE, model softmax outputs are encouraged to be uniform for OOD samples, which is enforced through the use of a KL divergence loss function. The first proposed modification in this paper is to replace KL divergence term with an L1 penalty. The second change is the addition of an L2 penalty between the maximum softmax probability and the model accuracy. Experimental results demonstrate that adding these two components increases performance over OE on standard OOD benchmarks for both vision and text domains, and also improves model calibration.
Although this paper presents some good quantitative results, I tend towards rejection in its current state. This is mainly due to the limited comparison to alternative methods, and the lack of ablation study. If these were addressed I would consider increasing my score.
Things to improve the paper:
1) Currently, one of the most commonly used benchmark methods for OOD detection is the Mahalanobis distance based confidence score (MD) [2], which, as far as I am aware, is state-of-the-art among published works. The authors claim that they do not compare to this work because it is a post-training method, and, presumably, the techniques should be doubly effective when combined. However, we do not have any proof that this is actually the case. Therefore, I think it is important to verify that the two techniques are indeed compatible, and if not, then direct comparison with MD would still be necessary.
2) In the case of confidence calibration, there is no comparison made with other calibration techniques, such as temperature scaling [3]. I think it would be good to included these for reference.
3) Since two distinct components are being added to the loss function, I think it is important to include an ablation study to identify how much each component contributes to improvements in OOD detection and confidence calibration.
Minor things to improve the paper that did not impact the score:
4) With regards to the confidence calibration loss, there is similar work by [4] which also optimizes the output of the model to make sure confidence predictions are close to the true accuracy. It may be worth citing if you think it is relevant.
Additional questions:
5) How are lambda1 and lambda2 tuned? I could not find this information in the paper.
6) How sensitive is model performance to the setting of the lambda hyperparameters? It would be nice to see a plot of lambda versus the OOD detection metrics.
7) Have you evaluated how this method performs at detecting adversarial attacks? I do not think the paper will suffer without these results, but they are certainly relevant and of interest to practitioners in this area.
References:
[1] Hendrycks, Dan, Mantas Mazeika, and Thomas G. Dietterich. "Deep anomaly detection with outlier exposure." ICLR (2018).
[2] Lee, Kimin, Kibok Lee, Honglak Lee, and Jinwoo Shin. "A simple unified framework for detecting out-of-distribution samples and adversarial attacks." NeurIPS, (2018).
[3] Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. "On calibration of modern neural networks." In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1321-1330. JMLR. org, 2017.
[4] Corbière, Charles, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, and Patrick Pérez. "Addressing Failure Prediction by Learning Model Confidence." NeurIPS (2019).
### Post-Rebuttal Comments ###
I would like to thank the authors for their hard work during the rebuttal period. I think the current version of the paper is much improved over the previous version. The choice to remove the claims about calibration definitely improves the focus of the paper. The addition of the  Mahalanobis distance experiments and the ablation study also significantly strengthen the paper. However, as the other reviewers have pointed out, the novelty of the paper is quite limited since the majority of the gains come from simply swapping the KL-divergence penalty with an L1 penalty. Despite simplicity, this single change yields a significant improvement in performance for the OE algorithm, which is noteworthy. As a result, I will increase my score from a weak reject (3) to a very, very weak accept (more like a 5 than a 6).