Can We Ignore Labels in Out of Distribution Detection?

Authors: Hong Yang, Qi Yu, Travis Desell
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Out of Distribution, Uncertainty, Self Supervised, Unsupervised

Abstract:
Out-of-distribution (OOD) detection methods have recently become more prominent, serving as a core element in safety-critical autonomous systems. One major purpose of OOD detection is to reject invalid inputs that could lead to unpredictable errors and compromise safety. Due to the cost of labeled data, recent works have investigated the feasibility of self-supervised learning (SSL) OOD detection, unlabled OOD detection, and zero shot OOD detection. In this work, we identify a set of conditions for a theoretical guarantee of failure in unlabeled OOD detection algorithms from an information-theoretic perspective. These conditions are present in all OOD tasks dealing with real world data: I) we provide theoretical proof of unlabeled OOD detection failure when there exists zero mutual information between the learning objective and the in-distribution labels, a.k.a. ‘label blindness’, II) we define a new OOD task – Adjacent OOD detection – that tests for label blindness and accounts for a previously ignored safety gap in all OOD detection benchmarks, and III) we perform experiments demonstrating that existing unlabeled OOD methods fail under conditions suggested by our label blindness theory and analyze the implications for future research in unlabeled OOD methods.

Decision: accept (poster)
Reviewer scores: 8, 5, 6

Reviews and discussion:
<thread>
creation date:
1729215963884

thread_type:
official_review

<thread_object>
creator: reviewer_yedc

<content>
summary:
This paper provides a theory to explain the failure of unlabeled OOD detection. The authors also define a new OOD task to test for label blindness and conduct experiments to verify the correctness of their theorem.

strengths:
[1] The paper is clear and easy to understand.

[2] The theory clearly explains the potential problem of unlabeled OOD detection.

[3] The theory is verified by the empirical studies, raising real concerns about unlabeled OOD detection.

weaknesses:
[1] While the main theory is sound in this paper, it is intuitive that the unlabeled OOD methods will fail if there is zero information between the learning objective and the label: For example, if we use linear regression to fit random noise, it is supposed to learn nothing. If the learning objective of the unlabeled OOD detection learns every feature but still has zero information with the label, then my understanding is that labeled OOD methods will also fail. Could the authors provide some comments on how the theory should be adapted in such a case?

[2] While Theorem 4.1 provides some justification on the effectiveness of the proposed Adjacent OOD task, there is a gap between Theorem 4.1 and the main theory: even if P(a randomly selected x contains y not present in $R_{y_{in}}$)>0, this is not exactly connected with the mutual information between the learning objective and the label. I would suggest the authors provide some more evidence, either theory or just simple simulation, to provide a more rigorous connection between the main theory and conclusion of Theorem 4.1.

[3] It is not clear how the theory can provide practical guidance on designing better learning objectives of unlabeled OOD. The paper provides the Adjacent OOD task, but this is different from suggesting a way of improving existing design: we know they fails, but we don't know how to improve them. Could the authors provide some comments on this?

[4] Please highlight the technical contribution when deriving the theorems. Since as elaborated in Weakness [1], the main theory is intuitive, it is not clear how difficult it is to prove it.

[5] Writing: please revise the wording in this paper to be more objective. For example, the word "you" or "your" appear 6 times in total.

questions:
Please address my comments in the weakness section.
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</thread>
<thread>
creation date:
1730268210503

thread_type:
official_review

<thread_object>
creator: reviewer_kvex

<content>
summary:
This paper mainly focuses on the Out-of-distribution (OOD) detection problem in self-supervised learning settings, which investigates the conditions for the theoretical guarantee of failure in unlabeled OOD detection from the information-theoretic perspective. In summary, this work first identifies one scenario in which any self-supervised learning or unsupervised learning algorithm will fail when there exists zero mutual information between the learning objective and in-distribution labels and then introduces a new detection task, namely adjacent OOD detection, which is theoretically proved to possibly exist in real-world dataset. Experiments with several existing self-supervised and unsupervised learning OOD methods are conducted to support the major claims of this work.

strengths:
1. This paper focuses on a new setting, i.e., self-supervised learning OOD, and investigates the feasibility of OOD detection from a theoretical perspective.
2. This paper theoretically proved the existence of failure conditions for unlabeled OOD detection algorithms, namely, label blindness, providing insights from the information-theoretic perspective.
3. This paper also introduces a new task, i.e., adjacent OOD detection, which is theoretically demonstrated to be possible in the real-world dataset.

weaknesses:
1. The writing and presentation can be further improved, since there are numerous typos and unclear notation or concept definitions. To list some, there is no clear introduction to the general paradigm of unlabeled OOD detection algorithms, and the preliminary about OOD detection is unclear in how to detect the OOD sample beyond using $P(y\notin Y_{in}|x)$.
2. The motivation and practical significance for considering self-supervised OOD detection is not clear. Given the current presentation and introduction content, the reviewer can hardly understand why we should conduct unlabeled OOD detection. If we have the pre-trained model on the ID data, even if we adopt the self-supervised learning in pertaining, can we have the pseudo label for those unlabeled data for OOD detection? It could be better to illustrate the overall research setting using some figure or framework before the existing Figure 1 for a better explanation.
3. It seems there are no explicit assumptions for the theoretical results and most theoretical results are based on the sufficiency definition in definition 2.1 and the minimal sufficient statistic in definition 2.2. It is questionable whether the theoretical insights reflect the real-world challenge or not, as there is limited empirical justification for those intermediate theoretical results based on mutual information. It could be better if the presentation incorporated more quantitative results regarding the mutual information.

questions:
1. It seems to be an unknown reference at the end of the first key contributions on Page 2.
2. Although the adjacent ood is discussed to be different from those previously defined near ood or far ood scenarios, what is the difference between adjacent ood with anonymous outlier detection if there is significant feature overlap at both settings?
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</thread>
<thread>
creation date:
1730694339028

thread_type:
official_review

<thread_object>
creator: reviewer_ezpk

<content>
summary:
The paper addresses challenges in out-of-distribution (OOD) detection, focusing on "label blindness," where OOD algorithms fail due to a lack of labeled data. It proves that unlabeled OOD detection inherently fails when label information is not linked to the learning objective, proposing an Adjacent OOD benchmark to capture these failures. Experimental results reveal that existing unlabeled methods struggle with overlapping IID and OOD data.

strengths:
- theoretical foundation proving that unlabeled OOD detection methods are inherently limited by "label blindness" -- a fundamental flaw in current self-supervised and unsupervised OOD approaches.
- novel OOD detection benchmark to test OOD methods under conditions of high overlap between in- and out-of-distribution data, filling a critical gap in safety evaluation.
- Extensive experiments that demonstrate current unlabeled OOD methods often fail in real-world scenarios with overlapping ID and OOD data, highlighting the need for label-aware approaches.
- Offers clear guidance for future OOD research by identifying key challenges and proposing benchmarks that better capture the limitations of current methods.

weaknesses:
- Limited applicability of theoretical claims -- while the paper introduces a "Label Blindness Theorem," rely heavily on specific assumptions about mutual information and independence (e.g., Theorem 3.1 and Corollary 3.3), which may limit the generalizability of the "label blindness" concept to real-world applications where these assumptions don't hold.
- the practical applicability of this theory is not fully explored and assume idealized conditions (Section 3.1) which might not reflect the complexity of real-world OOD scenarios.
- Relies heavily on specific datasets like ICML Facial Expressions, Stanford Cars, and Food-101, which limits generalizability, as these may not capture the diversity of OOD detection in other modalities such as language or video or audio.

questions:
- Could the benchmark be vulnerable to artificially low variance during testing due to potential biases in data selection or label overlap?
- It seems that the notion of of "label blindness" is central to the theory of this paper. How do you address practical situations where correlations between features and labels might enable OOD detection without explicit labels, and would this differ from the guarantees provided by you theoretical assumptions?
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</thread>
<thread>
creation date:
1732252720821

thread_type:
official_comment

<thread_object>
creator: area_chair_dwn9

<content>
comment:
Dear Authors and Reviewers,

The discussion phase has passed 10 days. If you want to discuss this with each other, please post your thoughts by adding official comments.

Thanks for your efforts and contributions to ICLR 2025.

Best regards,

Your Area Chair
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</thread>
<thread>
creation date:
1732295880967

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response

comment:
The authors would like to thank all reviewers for taking the time to provide insightful feedback. In this general response, we would like to highlight our answers to some key questions raised by reviewers: 

### Q1. Reviewers questioned the general applicability of theoretical claims due to the strict nature of the assumptions (eg. heavy reliance on independence and mutual information). The reviewers questioned if the assumptions would hold in real world applications (from **Reviewers  EZPK, kvex**). 

A1. The authors have extended the theory using Fano’s Inequality to consider cases where the learning objective and the labels are almost independent. Fano’s Inequality guarantees a lower bound on the prediction error, where the lower bound increases with a decrease in the mutual information between the learning objective and labels. This implies that when there is little mutual information, the lower bound of prediction error is extremely high. 

Such cases are now described as approximate label blindness to distinguish them from strict label blindness in the revised Section 3.1. The conditions in approximate label blindness better reflect the experiments conducted in this paper, where there is minimal mutual information between the learning objectives and labels. Through Fano’s inequality, we can apply the label blindness theorem to more real-world applications. 

The authors note that the experiments in this paper are conducted with the approximate label blindness settings, as there still exists some mutual information between the learning objective and labels in the datasets used in the benchmark. The approximate label blindness conditions may apply to many datasets and unlabeled OOD methods. 

### Q2. Reviewers questioned the impact of this paper on future research in unlabeled OOD detection and its applications. The reviewers noted that this paper identifies a theoretical problem present in unlabeled OOD detection, but does not provide a solution (from **Reviewers  EZPK, yEDC**). 

A2. The authors have improved the discussion section to provide guidance on how unlabeled OOD research can be improved. One method is to develop both labeled and unlabeled methods together, as was done in (Sehwag et al 2021). This allows the labeled alternative to be deployed where the unlabeled option cannot.

The authors also introduce discussion on the conditions in which one can safely deploy unlabeled OOD detection systems. Generally, these are cases where the assumptions of the theory do not hold (the learning objective and labels are strongly correlated) or when the risk of adjacent OOD data is sufficiently low or tolerable (if one could guarantee only near and far OOD detection). 

The paper has been updated to capture all the changes mentioned in our general response and detailed rebuttal to each review, where the changes are highlighted in blue. 

---
### References 
Vikash Sehwag, Mung Chiang, and Prateek Mittal. Ssd: A unified framework for self-supervised outlier detection. arXiv preprint arXiv:2103.12051, 2021.
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Metareview:
metareview:
Mainstream research in OOD detection focuses on classification problems that require a large amount of labeled data. Obtaining labeled data is a well-known burden for small companies or universities. Thus, this paper focuses on OOD detection in a self-supervised learning setting where is more often considered in the real world. The paper provides an essential analysis of this problem and proposes a novel algorithm in the end. The performance is validated via experiments as well. 

Only one reviewer questions the significance of this problem. However, after checking the authors' responses and the paper itself, the studied problem is interesting and has a certain value to the field. The authors should merge all comments into the next version of this paper.