. At the end of section 2, the authors tried to explain noisy signals are harmful for the OOD detection. It's obvious that with more independent units the variance of the output is higher. But this affects both ID and OOD data. The explanation is not clear.
. The analysis in section 6 is kind of superficial. 1) Lemma 2: the conclusion is under the assumption that the mean is approximately the same. However, as DICE is not designed to guarantee this assumption, the conclusion in Lemma 2 may not apply to DICE. 2) mean of output: the scoring function used for OOD detection is max_cf_c(x). The difference of mean is not directly related to the detection scoring, so the associated observation may not be used to explain why the algorithm works.
. Overall, it is not well explained why the proposed algorithm would work for some OOD detection. 1) From the observation, although DICE can reduce the variance of both ID and OOD data, the effect on OOD seems more significant. This may due to the large difference between ID and OOD. Therefore, it would be interesting to exam the performance of DICE by varying the likeness between OOD and ID. 2) From Figure 4, the range of ID and OOD seems not to be changed much by sparsification. Similarly, Lemma 2 requires approximately identical mean as the assumption. These conditions are crucial for DICE, but is not well discussed, eg., how to ensure DICE meet these conditions.
. In the experiment, the OOD samples generally are significantly different from ID samples (thus less challenging). As pointed out in the above comment, it would be interesting to compare the performance of DICE by varying the OODness of test samples. For example, the ID data is 8 from MNIST, OOD datasets can be 1) 3 from MNIST; 2) 1 from MNIST; 3) FMNIST; and 4) CIFAR-10.
. The comparison between DICE and generative-based model (Table 3) is unfair as DICE is supervised while the benchmarks are unsupervised. It's not surprising that DICE is better. The authors should add comments on that.
. It is claimed in the experimental part that the in-distribution classification accuracy can be maintained under DICE. Only the result on CIFAR-10 is shown. Please provide more results to support the conclusion if possible.
. Instead of using directed sparsification, one possible solution may be just using a simpler network. Of course this would change the original network architecture. But as one part of the ablation study, it would be interesting to know whether a simpler network would be more beneficial for the OOD detection.
2) From Figure 4, the range of ID and OOD seems not to be changed much by sparsification. Similarly, Lemma 2 requires approximately identical mean as the assumption. These conditions are crucial for DICE, but is not well discussed, eg., how to ensure DICE meet these conditions.