Title: Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Out-of-Distribution Detection

Spurious Correlations

Prototype Refinement

Robustness

Post-hoc Methods

Trustworthy AI
Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications, where they frequently face data distributions unseen during training.
Despite progress, existing methods are often vulnerable to spurious correlations that mislead models and compromise robustness. To address this, we propose SPROD, a novel prototype-based OOD detection approach that explicitly addresses the challenge posed by unknown spurious correlations.
Our post-hoc method refines class prototypes to mitigate bias from spurious features without additional data or hyperparameter tuning, and is broadly applicable across diverse backbones and OOD detection settings.
We conduct a comprehensive spurious correlation OOD detection benchmarking, comparing our method against existing approaches and demonstrating its superior performance across challenging OOD datasets, such as CelebA, Waterbirds, UrbanCars, Spurious Imagenet, and the newly introduced Animals MetaCoCo. On average, SPROD improves AUROC by 4.8%  and FPR@95 by 9.4% over the second best.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses spurious OOD and non-spurious OOD where the former contains spurious contexts combined with lack of features, and latter lacks both in an OOD setup. The authors suggest SPROD, a method which seeks a robust prototypes of ID classes in a 3 stage manner: seeking initial prototype, discovering minor prototype, and refining prototypes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengthness\n1. The paper brings up a less discussed topic, spurious OOD, which tackles to decrease the misleading correlation between spurious features and the object itself. \n2. Proper experiments were conducted to support the argument in the paper.\n3. Authors have conducted extensive amount of experiments.  \n\nWeakness\n1. Authors do not compare with recent baseline methods.\n2. This work should conduct experiments on general OOD datasets where the reasons are stated below.\n\nQUESTIONS:\n1. Compared baselines in Table 2 are outdated. The latest work is done on 2022 while the submission date was mid 2025. Comparing with recent methods which are published since 2023 must be done. Especially, there is a github repository with numerous recent methods on https://github.com/Jingkang50/OpenOOD such as CombOOD, SCALE, AdaSCALE, NNGuide, ASH, GEN, RELATION, and more must be conducted and added to all Table 2. These should be done on general OOD test datasets (CIFAR10, CIFAR100, ImageNet) as well.\n2. OOD fundamentally deals with “unknownness” without any given prior knowledge related with OOD  samples which show up on testing. However, this work only conducts experiments on a strong prior OOD test dataset, where the datasets are designed explicitly related with “spurious” features. Only testing on datasets with such strong assumption hinders the practicality and fundamental reason of researching OOD detection. If SPROD works only well on spurious OOD setup, I doubt the practicality of the method as OOD samples wouldn’t be labeled as “spurious” when they are tested. Therefore, experiments on CIFAR10, CIFAR100, and ImageNet must be conducted in order to prove the robustness and show that SPROD is not only limited to spurious OOD dataset, but also robust on general OOD datasets.\n3. In table 2, results of FPR95 of SPROD is only the 4th comparing with other methods for the most complex dataset which is hard to insist it is the SOTA method.\n4. Figure 4 doesn’t represent well how much the correlation between spurious correlation rate and performance drop as the results aren’t presented numerically. It doesn’t look like the best way to present the correlation as a reader.\n5. The authors argue that generative scoring is better than discriminative scoring and present proper experimental configurations. However, results show no difference when datasets get more complex. Showing better performance only on simple dataset doesn’t indicate it is a better scoring method. Therefore, I doubt the fact of this statement.\n6. Table 1 in Supplementary should be in the main manuscript as it is comparing with related methods.\n\nOverall, my biggest concerns are 1. and 2. whose experiments should be done. Change of review results would highly depend on the results of these two questions.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work tackles the problem of OOD detection considering the OOD samples that share spurious features with the ID samples but differ in semantic labels. Specifically, the samples belong to the same classes are grouped into 1)  a group of correctly classified samples 2)  multiple groups of misclassified samples depending on which classes they are misclassified.  Following that, prototypes are calculated for all groups.  Subsequently, all samples are reassigned to each group depending on the proximity to the corresponding prototypes. Finally, the refined prototypes are computed as the mean of updated group members. During inference, OOD score is calculated based on  the distance to the nearest group prototypes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper is well-written, and the proposed Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution 159 Detection (SPROD) is well-motivated. \n- The experiments on spurious OOD (SP-OOD) benchmark are comprehensive, and the performance is significant.\n\nWeaknesses:\n-   The results on non-spurious OOD (NSP-OOD) is missing. I understand that this work mainly focuses on the SP-OOD, however, i am wondering that whether the proposed SPROD is also effective on NSP-OOD, or at least maintain the performance on NSP-OOD.  This would significantly improve the contribution of this work. Therefore, the results on NSP-OOD are expected to present.  I will raise the score once this concern is addressed.\n- To demonstrate the generalization of proposed methods, the evaluate across different models such as ViT and BiT are expected to present. \n- Some baseline methods are missing such as GEN [1], NN-Guide [2], NECO[3] and FDBD[4]. \n- What is the (synthetic) datasets and model utilized to produce Figure 2?\n\n[1] GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection, CVPR, 2023.\n\n[2] Nearest Neighbor Guidance for Out-of-Distribution Detection, ICCV, 2023.\n\n[3] NECO: NEural Collapse Based Out-of-distribution detection, ICLR, 2024.\n\n[4] Fast Decision Boundary based Out-of-Distribution Detector, ICML, 2024.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a prototype-based OOD detection approach explicitly designed to address the challenge posed by unknown spurious correlations. The proposed method, SPROD, is a post-hoc technique that refines class prototypes to mitigate potential bias. For each test sample, the distance to the nearest prototype is used as the score for OOD detection. Comprehensive experiments on spurious correlation OOD detection benchmarks demonstrate superior performance compared to several standard OOD detection methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n* The paper is well-written and easy to follow. It clearly demonstrates the problem and will serve as a valuable resource for the OOD detection community.\n\n* The proposed method is straightforward and, in that sense, simple: it involves refining prototypes (similar to K-means clustering) and using the distance to the nearest prototype as the score function.\n\n* The presented experiments highlight the strength of the proposed approach, showing consistent improvements over 11 post-hoc baselines. The paper also includes other useful analyses, such as investigations into the scoring mechanism.\n\nWeaknesses:\n\n* My main concern is the lack of discussion or comparison with prior prototype-based OOD detection work. In recent years, several studies have explored prototype-based frameworks, and in this context, the proposed method does not appear significantly novel. The main contribution seems to lie in its focus on spurious OOD detection, rather than in introducing a fundamentally new technique.\nSome relevant examples include:\n1. Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes\n2. Prototype-Based Optimal Transport for Out-of-Distribution Detection\n3. Learning with Mixture of Prototypes for Out-of-Distribution Detection\n\n* While the empirical results demonstrate the effectiveness of the method, it is not entirely clear how or why the group prototype refinement specifically helps in addressing spurious OOD detection. A deeper theoretical or conceptual justification would strengthen the contribution.\n\nQUESTIONS:\nSuggestions: It is recommended that the authors explicitly define the score function used in SPROD, rather than merely stating it.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work defines two ood categories, namely SP-OOD and NSP-OOD. In response to SP-OOD, the paper proposes that SPROD is a use of three stages to solve the SP-OOD problem which has spurious correlations. It strives to conduct reinforcement learning on the samples misclassified by the classifier and construct more detailed minority group prototypes to enhance the learning ability of the model for SP-OOD. In the experiment, five OOD datasets were defined for NSP-OOD as the ood evaluation datasets for SP-OOD, and it was verified that the conventional softmax score was not suitable for them, while the likehood score was more suitable for them.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths:\n\n1. The paper proposes a set of post-hoc methods for SP-OOD. This method uses the class labels of each image to enhance the classification error of the training samples through sample reinforcement learning to construct more detailed minority group prototypes.\n\n2. The article presents the suitable OOD score forms for their post-hoc method and experimentally presents the results of the generative score and discriminative score, explaining the reasons why the softmax OOD score is not suitable.\n\nWeaknesses:\n1. Although the article pays more attention to the experimental performance of SP-OOD, we still hope that the author can provide some experimental results on the NSP-OOD and conventional OOD datasets. The real-world OOD dataset should be a combination of multiple OOD data. We can see that this method in the paper can improve the detection performance of SP-OOD without sacrificing the detection performance of other OOD types.\n\n2. The article lacks some ablation experiments. The author can supplement some relevant ablation experiments, such as proving the effectiveness of stage 2 and stage 3.\n\n3. In Figure 3 of the article, the square ICONS and other items are not specified.\n\n4. The most recent competing methods are missing in this work, such as CLIP-based methods.\n\nQUESTIONS:\n1. Why is the performance of ImageNet100 lacking on the more common OOD datasets? Is it related to the number of categories or for other reasons? It is suggested that the authors conduct an analysis.\n\n\n2. Currently, the application of CLIP in OOD detection is a research hotspot. I wonder how the proposed method can be used in OOD detection based on CLIP. Because the comparison method in the article lacks the comparison of 2023-2025 methods in recent years.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-04"}