To reliably detect the out-of-distribution images based on already deployed convolutional neural networks, several recent studies on out-of-distribution detection have tried to define effective confidence scores without retraining the model. Although they have shown promising results, most of them need to find the optimal hyperparameter values by using a few out-of-distribution images, which eventually assumes a specific test distribution and makes it less practical for realworld applications. In this work, we propose a novel out-of-distribution detection method MALCOM, which neither uses any out-of-distribution samples nor retrain the model. Inspired by the observation that global average pooling cannot capture the spatial information of the feature maps from convolutional neural networks, our method aims to extract informative sequential patterns from the feature maps. To this end, we introduce a similarity metric which focuses on the shared patterns between two sequences based on normalized compression distance. In short, MALCOM uses both the global average and spatial pattern of the feature maps to accurately identify out-of-distribution samples.
** post rebuttal start **
After reading reviews and authors' response, I decided not to change my score.
I remark that this paper still requires a lot of revision; comparison in the main paper is somewhat unfair and all new results are in the appendix.
Also, the performance of their replication of the prior method is far lower than reported. In worst case, the performance gain from the compared method would be from their incorrect implementation on the prior works. In this kind of case, I suggest the authors to put {the numbers in the original paper} as well as {their replication} and claim that they fail to replicate the number. Ideally, if their method is evaluated in the same condition, it should outperform prior works in any case.
Detailed comments:
1-(a). Adversarial attack and OOD (which is hard to detect) are closely related: they are both in off-manifold. Their main difference would be, while adversarial attack is very close to the clean data in the data space, OOD is relatively far from the in-distribution in the data space.
Though it does not talk about OOD, you may refer to [R1] for analysis in perspective of manifold. The difficulty of OOD detection can be considered to be coming from overlapped manifolds in the latent space.
[R1] Stutz et al. Disentangling Adversarial Robustness and Generalization. In CVPR, 2019.
1-(b). Though the numbers are much lower than those reported in the original paper, I am happy to see the fair comparison. However, according to the original paper, simple FGSM is used for validation, so I am not sure such a huge difference can actually happen. In this kind of case, I suggest the authors to put {the numbers in the original paper} as well as {their replication} and claim that they fail to replicate the number.
2. I am happy to see that their revised method has better performance.
** post rebuttal end **
- Summary:
This paper proposes an out-of-distribution detection (OOD) method under constraints that 1) no OOD is available for validation and 2) model parameters should be unchanged. They specifically address a problem of the state-of-the-art method satisfying the constraints, and propose a new distance metric inspired by data compression. Experimental results on several benchmarks with different deep neural network architectures support their claim.
- Decision and supporting arguments:
Weak reject.
1. The problem setting is clear and their approach is interesting and makes sense. However, the method for comparison is not properly set. As the authors addressed, Mahalanobis detector proposed by Lee et al. (2018b) requires validation to determine weights for feature ensembling, but the validation can be done without OOD data by generating adversarial samples as proposed in the same paper. Although Table 2 in Lee et al. (2018b) shows that the performance is not better than the case when we have an explicit OOD data for validation, it reasonably works well. Therefore, rather than comparing with the vanilla version (only using last latent space) or the alternative "assemble" method (concatenating all average-pooled features), they had to compare their method with the model validated by adversarial samples, which essentially satisfies the constraints.
2. Also, I wonder the main body of the proposed method itself is really effective or some minor tweak they made is essential. According to Table 2 in the submission, their method is better than "Mahalanobis vanilla" only when all components are applied. Though the idea is interesting, I am skeptical about the effectiveness of the proposed method.
- Comments:
1. As addressed by the authors, feature concatenation ("assemble") is not effective for the Mahalanobis method but the proposed method. How about to do an ablation study about weighted averaging vs. concatenation on the proposed method as well? Again, weights can be validated by adversarial samples to satisfy the constraints.