1.The innovation of the article seems limited to me, mainly since the work shares the same perspective as [2]. Both the models build upon the probabilistic formulation and applies the Hilbert-Schmidt Independence Criteria (HSIC). It may be good to clarify a bit more on how novel the paper is compared from [2].
2.There is a lack of qualitative experiments to demonstrate the validity of the conditional independence model. a)It is better to provide some illustrative experimental results to demonstrate that minimising HSICcond-i could indeed perform better than minimising HSIC_HOOD. Possibly, one toy dataset can be used to demonstrate the separability of inlier features and outlier features.
b)The authors propose a new test metric, however, lacking the correctness test and comparative experiments with other metrices. It may be better to provide some visualization results or schematic diagram, which could make readers easier to understand.
3.Current experimental results seem not very convincing to me. Some critical comparative results are missing. a)Under the setting of unseen OOD training data, DIN [34], Mahalanobis distance [31], Energy [36], their original papers did not use fake/augmented OOD training data. These settings need to be clarified in the paper. Moreover, the impact of using different augmentation methods on the result could be explored in the ablation.
b)In CIFAR-100, the experimental setup appears to be consistent with that of HOOD [2]. However, in Table 1 (unseen OOD training data), the HOOD’s results are missing. In [2], the results of HOOD are superior to those of Conditional-I's.
c)In Table 2 (unseen OOD training data), the HOOD’s results are also missing.
d)There are missing both Conditional-i-generative and HOOD results for the NLP OOD detection tasks. As missing the results of the most relevant methods, the present experiments could not convince me of the validity of the improvements.
4.The memory bank architecture is one contribution, but the authors do not provide quantitative results of introducing the memory bank architecture.
The authors seemed not to discuss the limitations of the proposed model.
2.There is a lack of qualitative experiments to demonstrate the validity of the conditional independence model. a)It is better to provide some illustrative experimental results to demonstrate that minimising HSICcond-i could indeed perform better than minimising HSIC_HOOD. Possibly, one toy dataset can be used to demonstrate the separability of inlier features and outlier features. b)The authors propose a new test metric, however, lacking the correctness test and comparative experiments with other metrices. It may be better to provide some visualization results or schematic diagram, which could make readers easier to understand.