AHA: Human-Assisted Out-of-Distribution Generalization and Detection

We thank you for the detailed comments and questions, which we address in detail below.

W1. While the paper demonstrates strong results, it is not clear how the AHA framework scales with larger and more complex datasets.

We tested the AHA framework on the larger and more complex ImageNet benchmark. We use ImageNet-100 as the ID data, ImageNet-100-C with Gaussian noise as the covariate OOD data, and high-resolution natural images from iNaturalist for the semantic OOD data. Our AHA method still achieves much better performance in OOD detection in terms of FPR95 and AUROC compared to WOODS and SCONE.

Results for both OOD generalization and OOD detection are summarized below:

W2. The reliance on human annotations could be a limitation in scenarios where such resources are not readily available or are cost-prohibitive.

This is a valid point. There may indeed be special scenarios where resources are not available or are cost-prohibitive. We will include this in the limitations discussion section. However, our proposed AHA framework is label-efficient, requiring only a small portion of labels (1% $\sim$ 2%) to be effective, which significantly reduces the cost associated with human annotation requirements.

W3. The paper could benefit from a discussion on how the findings generalize beyond the tested datasets and scenarios.

We thank you for the suggestion. The findings may not generalize to special scenarios where there is no clear boundary between covariate-shift and semantic-shift data, and these two types of OOD samples can be very hard to separate. In such cases, the proposed AHA method might downgrade and become comparable to random sampling. However, in most real-world scenarios, there are usually discernible differences between covariate and semantic shifts.

W4. The computational complexity of the AHA algorithm and its runtime performance on large datasets are not discussed.

The computational complexity of the AHA algorithm is $O(Nk)$, where $N$ denotes the number of examples and $k$ denotes the budget size. The average runtime for the core part of the noisy binary search process in the AHA algorithm is 0.3232 milliseconds per point. For a large dataset, for example when the dataset size is 60k, the total runtime performance for the noisy binary search step is around 19 seconds. This step is inexpensive compared to neural network training. For the last step, training of the multi-class classifier and OOD detector with selected and annotated examples based on the loss objective in Section 4.4 usually takes several hours to converge.

W5. The paper could address potential biases introduced by human labeling, especially in the context of OOD detection.

Here are the specific steps taken to mitigate potential biases in human labeling: We propose using diverse labelers and providing thorough training for them on recognizing and avoiding biases. Additionally, we will implement validation procedures to ensure label quality, such as having a subset of data labeled by multiple people and utilizing expert reviewers. These steps could help to reduce the potential biases introduced by human labeling.

Moreover, our framework is label-efficient and only requires 1% $\sim$ 2% annotations for the wild data. This makes it practical for manual double verification to address potential biases and ensure label quality, even in cost-prohibitive scenarios.

Q4. How does the framework handle a class imbalance in the context of OOD detection?

Our framework uses metrics such as AUROC, which are less sensitive to class imbalance and provide a more accurate assessment of performance even in imbalanced scenarios.

Q5. Are there any specific domains or applications where the AHA framework is expected to be more or less effective, and why?

Referring to our response to W3, the AHA framework is expected to be less effective in rare scenarios where there is no clear boundary between covariate OOD and semantic OOD data, as these two types of OOD samples can be very hard to separate. We will add this discussion to the main paper.

Q6. Could the proposed method benefit the OOD detection with unreliable sources [R1] and inspire unsupervised OOD detection [R2]?

This could be part of our future work to extend the AHA framework by considering unreliable sources and unsupervised OOD detection. We will cite and incorporate these two literature in the future work discussion section.

We thank you for your thorough comments and questions, which we address in detail below.

W1. AHA may only work under a rather strict assumption.

We would like to clarify that the unlabeled wild data distribution $S_{\text{wild}}$ is totally different from the test distribution. Our driving motivation is to exploit unlabeled wild data with various distribution shifts naturally arising in real-world scenarios. Thus, we consider the following generalized characterization of the wild data to model the realistic environment:

$P_\text{wild}:= (1-\pi_s-\pi_c) P_\text{in} + \pi_c P_\text{out}^\text{covariate} +\pi_s P_\text{out}^\text{semantic}$

Moreover, outlier exposure has a more strict assumption of careful data cleaning to ensure the auxiliary outlier data does not overlap with the ID data. Compared to outlier exposure, we relax this assumption by leveraging unlabeled "in-the-wild" data, which is a mixture of ID, covariate OOD, and semantic OOD data commonly observed in real-world applications.

W2. I generally believe enhancing OOD detection and generalization with human feedback is laborious.

Our method significantly reduces the labeling effort compared to traditional approaches. With no more than 2% annotations of the wild data, our method outperforms existing state-of-the-art methods by reducing OOD detection error by 15.79% and increasing OOD generalization accuracy by 5.05%.

Regarding the CIFAR experiments, while the images are small (32x32), our method only requires binary "In" vs. "Out" labels for the selected semantic OOD data, and category labels for the covariate OOD data. This simple classification task is much faster than providing detailed labels or descriptions, significantly reducing the time and effort needed from human annotators.

We also note that labeling a few hundred OOD samples is generally much cheaper than labeling in-distribution examples. For reference, labeling 200 images with five labelers each costs less than $10 on Amazon Mechanical Turk. We believe the trade-off between this minimal labeling effort and the substantial performance improvement, especially in challenging OOD scenarios, makes our approach particularly efficient and practical for real-world applications.

W3. The experiments are not adequate. It is widely acknowledged that OOD detection and generalization are more difficult on large-scale high-resolution datasets.

The experiments on large-scale, high-resolution datasets are included in Appendix G. Following the ImageNet benchmark for joint OOD generalization and OOD detection as used in SCONE literature [1], we use ImageNet-100 as the in-distribution data, with labels details provided in Appendix E. For the covariate OOD data, we use ImageNet-100-C with Gaussian noise in the experiment. For the semantic OOD data, we use the high-resolution natural images from iNaturalist.

Results for both OOD generalization and OOD detection evaluation are summarized below:

These experiments demonstrate that our method maintains its effectiveness on more complex datasets.

[1] Feed Two Birds With One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. ICML 2023.

W4. In some cases, there may be no clear boundary between covariate-shift data and semantic shift data.

We acknowledge the valid point raised about the potential difficulty in distinguishing between covariate-shift and semantic-shift data in some cases. However, in such scenarios, our proposed AHA method would naturally default to unbiased sampling, performing no worse than a random sampling strategy.

Moreover, it's important to note that such cases are relatively rare in practice. Most real-world scenarios do exhibit discernible differences between covariate and semantic shifts, as verified empirically in Section 5.4 of our main paper.

Thank you for taking the time to read our response. We address your comments below.

1. I still have further concerns about the basic settings of this paper.

We would like to clarify that the wild data setting, which includes a mixture of ID, covariate OOD, and semantic OOD data, is commonly observed in practice. The overall wild mixture data distribution $P_\text{wild}$ differs from the test environment data distribution. Additionally, the mixing ratios of $π_s$ and $π_c$ are unknown in our formulation, making this setting well-suited to real-world scenarios with varied distributions of wild data. We have also empirically tested different mixing ratios, as detailed in Appendix H, where we demonstrate the robust and strong performance of our AHA framework.

Moreover, as suggested, we have summarized the OOD accuracy results on covariate test data when the model is exposed to one type of corruption but tested on another. Specifically, we exposed the model to wild data containing Gaussian noise corruption and then tested it on nine other types of corruption: impulse noise, spatter, shot noise, saturate, speckle noise, frosted glass blur, motion blur, frost, and zoom blur. The results indicate AHA displays strong performance, even when the test-time covariate-shifted distribution remains unknown.

2. Regarding the argument for W2.

Thank you for your constructive comments. We will incorporate discussions about the cost of additional labeling in the paper as suggested.

Thanks for your response and hard work during the rebuttal. I am pleased to acknowledge that this paper has no significant flaws and the additional experiments are well-appreciated. However, I agree with other reviewers that the need for human assistance is both an advantage and an inevitable weakness of AHA. The uncommon settings should also be carefully explained and compared with classic domain adaption or OOD generalization. It seems to be a rather strict assumption than those used in domain adaption or OOD generalization (accessing samples drawn from test-time covariant-shift distribution and labeling some of them). Removing such an assumption can greatly strengthen the proposed method (e.g., involving various types of corruption). Theoretical support of why unlabeled data can help both OOD detection and generalization in AHA can be also taken into consideration in future work. With a better understanding of this paper, I have adjusted my score accordingly.

We thank you for your positive feedback and comments. We address each comment below in detail.

W1. There exists a strong assumption that the weighted densities of semantic and covariance ood should equalize

Thank you for pointing out this potential misunderstanding. We would like to clarify that our formulation does not require the strong assumption that the weighted densities of semantic and covariate OOD should be equal. This holds for both our maximum disambiguation region formulation (Section 4.2) and the AHA algorithm (Section 4.3).

Specifically, our maximum ambiguity threshold formulation in Eq. (1):

$\lambda_* = argmax_{\mu \in R} \int_0^\mu ((1-\pi_c -\pi_s)p_{\text{in}}(\nu) + \pi_c p_{\text{covariate}}(\nu)) - \pi_s p_{\text{semantic}}(\nu) d\nu$

indicates that if the density of covariate OOD is much smaller than semantic OOD, we would choose the smallest OOD scoring value. Conversely, if the density of covariate OOD is much larger than semantic OOD, we would choose the largest OOD scoring value, where we try to sample as many semantic OOD examples as possible.

W2. What is difference between active learning and the proposed Human-Assisted Learning

Human involvement in labeling data points has been studied under various terminologies, including human-in-the-loop learning, bandits, interactive learning, and active learning. The title "Human-Assisted Learning" stems from our finding that human assistance can dramatically improve OOD generalization and detection performance.

We provide discussions on active learning in Appendix C. To offer more context here, classic active learning involves an iterative training process, while our proposed algorithm only requires a single model fine-tuning. Additionally, existing deep active learning works do not study OOD robustness and the challenges posed by realistic scenarios involving wild data. Our proposed AHA method is specifically tailored for both OOD generalization and detection challenges.

W3. What is the time used for noisy binary search

The average time for the noisy binary search process is 0.3232 milliseconds per point on Tesla V100 GPU.

W4. Whether the lamda searched on the one dataset can be transfer to another dataset

The lambda searched on one dataset is not transferable to another dataset due to variations in data distributions. While lambda itself can't be transferred, the process of searching for the optimal lambda for each dataset is efficient and not computationally expensive. For example, the processing time for a dataset with 60k samples is about 19 seconds when using the Tesla V100 GPU.