AHA: Human-Assisted Out-of-Distribution Generalization and Detection
Authors: Haoyue Bai
Jifan Zhang
Robert D Nowak
Venue: NeurIPS 2024 poster (2024)
Keywords: OOD Generalization
OOD Detection
Human-Assisted
Primary area: safety_in_machine_learning

Abstract: Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These shifts give rise to challenges in OOD generalization and OOD detection. This paper introduces a novel, integrated approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild. Our approach strategically labels examples within a novel maximum disambiguation region, where the number of semantic and covariate OOD data roughly equalizes. By labeling within this region, we can maximally disambiguate the two types of OOD data, thereby maximizing the utility of the fixed labeling budget. Our algorithm first utilizes a noisy binary search algorithm that identifies the maximal disambiguation region with high probability. The algorithm then continues with annotating inside the identified labeling region, reaping the full benefit of human feedback. Extensive experiments validate the efficacy of our framework. We observed that with only a few hundred human annotations, our method significantly outperforms existing state-of-the-art methods that do not involve human assistance, in both OOD generalization and OOD detection.

AHA: Human-Assisted Out-of-Distribution Generalization and Detection
Haoyue Bai, Jifan Zhang, Robert Nowak
University of Aaaconsin-Madisee (baihaoyue, jifan]0cs wisc. edu, rdnowak@wisc. edu
Abstract
Modern machine learning models deployed often encounter distribution shifts real-world applications, manifesting as covariate or semantic out-of- of-distribution (OOD) shifts. These shifts give rise to challenges in OOD generalization and OOD detection. This paper introduces novel, integrated approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD general- ization and detection through the wild. Our approach strategically labels examples within novel maximum disambiguation region, where the number of semantic and covariate OOD data roughly equalizes. By labeling within this region, we can maximally disambiguate the types of OOD data, thereby maximizing the utility of the fixed labeling budget. Our algorithm first utilizes noisy binary search algorithm that identifies the maximal disambiguation region with high probability. The algorithm then continues with annotating inside the identified labeling region, reaping the full benefit of human feedhack. Extensive experiments validate the efficacy of framework. We observed that with only a few hundred human annotations, our method significantly outperforms existing state-of-the-ar methods that do not involve human assistance, in both OOD generalization and OOD detection. Code is publicly available http://///gittub..oo//aa///a///
framework by labeling data in
1 Introduction
Modern machine learning models deployed in the real world often encounter various types of distribution shifts. For example, -ittr--distribution (OOD) covariate shifts arise when the domain and environment of the test data differ from the training data. OOD semantic shifts occur when the model encounters novel classes during testing. This gives rise to two important challenges: OOD generalization [2] T which addresses distribution mismatches between training and test data related covariate shifts, and OOD detection [46] 62. 90], which aims to identify examples from semantically unknown categories that should not be predicted by the classifier, relating to semantic shifts. The natural coexistence of these different distribution shifts in real-world scenarios motivates the simultaneous handling of both tasks, direction that has not been largely explored previously, as most existing approaches are highly specialized in one task.
Specifically, we consider a generalized characterization of the wild data setting 6 that naturally arises in the model's 's operational environment:
Pwild == (1 n. IP + Wel out
peovariate
T. out
psemantic
where in,, denotes the marginal distributions of in-distribution (ID) data, peovariate represents covariate- shifted OOD data, and Ipeeanntic indicates semantic-shifted OOD data. This challenging lack access to both the category labels and distribution types of this wild mixture data, which crucial for OOD learning. To tackle this challenge, it is natural to develop auman-assisted framework and
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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ID
Maximum disambiguatio bfu region
Covariate OOD
S Semantic OOD
Covariate 00D
Semantic OOD
Covariaty
Semantic
Budget k
Budget K
Budget Maximum ambiguity threshold
95% threshold
(a) Top-k most OOD examples
(b) Near-boundary region
(c) Maximum disambiguation re- gion (Ours)
Figure I: Illustration and comparison three different labeling regions. The horizontal axis is OOD score, and the vertical axis is the frequency. Note that we color the three different sub-distributions (ID, covariate OOD, semantic OOD) separately for clarity. In practice, the membership is not revealed on these unlabeled wild data.
selectively label a set of examples from the wild data distribution. These examples are then used to train a multi-class classifier and an OOD detector. A critical yet unresolved question thus arises:
By leveraging human feedback, can we identify and label a small set of examples that significantly enhances both OOD generalization and detection?
In this paper, we propose the first algorithm AHA (Adaptive Human-Ass Asisted OOD learning) that incorporates human assistance in improving both OOD generalization and detection together. Given a limited labeling budget, our strategy selects wild examples that predominantly exhibit covariate shifts or semantic shifts, as these are the most informative for improving a model's OOD generalization and detection performances. At the core of our approach, we identify novel labeling region, the maximum disambiguation region. Within this region, the densities of covariate OOD and semantic OOD examples approximately equalize, making it difficult for the OOD detector to differentiate between the two types of OOD data.
As demonstrated in Figure c)), the maximum disambiguation region is centered around the maximum ambiguity threshold, where the densities of the two types of OOD data exactly equalize. By labeling examples around this threshold, we therefore also maximize the total human corrections to the given OOD detector. Naturally, our algorithm invokes a two-phased procedure. First, we address the challenge of identifying the maximum ambiguity threshold by framing it as noisy binary search problem and utilize an off-the-shelf adaptive labeling algorithm [56]. For the second phase, we label equal number of examples adjacent to the identified threshold from both sides.
Extensive experiments demonstrate the efficacy of AHA for both OOD generalization and detection. We observe that with only few hundred human annotations, AHA notably improves OOD general- ization and detection over existing SOTA methods that do not involve human assistance. Compared to most related literature 66) our findings indicate that obtaining just few hundred human labels can reduce the average OOD detection error by 15.79% in terms of FPR95, and increase the accuracy of neural networks on covariate OOD data by 5.05% (see Table D
Table 1: Results highlight: comparison with state-of -hee------h-attt art method SCONE on CIFAR-10 benchmrk.
SVHN pog CIEAR 10-C Stayekie
LSUN-C C Jogeere
CIFAR- 10-C
Teature Pog CIFAR I0-C p0:
Methed
0OD Acc.f
ID Acc.T
EPR]
AUROC!
0OD Acc.1 10 Acc.7
FPR.
AUROC
OODDAce. Acc."
10 Acc.
EPR.
AUROCT
SCONE
$4.89
94
10.86
97.84
$4.58
93.73
10.23
85.56
37,15
90:91
Ours
89.01
94.67
0.08
99.99
90.69
94.45
0.02
99.95
98.51
98.54
5.63
97.95
Places PICERR, CIFAR-10.C Posite
LSUN R. Jureer CIFAR 10-C
Average
Meched
OOD Are.T
ID Anc?
FPR]
AUROCI
000 Aec.1
Aec.
FPR.
AUROC:
OOD Ave."
ID Aec.7
FPR]
AUROCI
SCONE
85.21
94.59
37.56
90.90
80.31
94.97
1187
99.79
84.95
94.32
19.33
95.49
Ours
38,93
94.30
11.88
95.60
90.86
94.32
0007
99.98
90,00
94.46
3.54
98.70
Our key contributions are:
We are the first to leverage human assistance in improving both OOD generalization and detection, offering a natural and effective approach for labeling wild data with heterogeneous data shifts. We propose novel labeling strategy that targets the maximum disambiguation region, which significantly enhances both OOD generalization and detection when labeled. Extensive experiments and ablation studies demonstrate the effectiveness of our human-assisted method. AHA shows robust performance in both OOD generalization and detection.
2
Related Works
-utto--dist generalization an important and challenging problem in machine learning, arising when there are distribution shifts between the training and test data. Compared to traditional

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domain adaptation tasks [2 36 89 57 32 99 74], OOD generalization is more critical as it focuses
on generalizing to covariate-shifted data distributions that are unseen during training g 61 T14
71
69
58 A primary set of approaches to OOD generalization involves extracting domain-
invariant representations. Strategies include invariant risk minimization 2 [9] 111 I. domain
adversarial learning [65, 115, 86 37 666, meta-learning [63] 76), and others [78 16 7 Other
sets of approaches for OOD generalization include single domain generalization [75] 92), test-time
adaptation [52] [10), and model ensembles B 777. SCONE (6] aims to enhance OOD generalization
and detection by leveraging unlabeled data from the wild. Based on the problem setting of SCONE, we propose to integrate human assistance to enhance OOD robustness and improve OOD detection accuracy. The primary motivation is to identify the optimal labeling regions within the wild data. We find that even few hundred human-labeler instances, chosen based our selection criteria, can significantly enhance performance for both tasks.
cittof-distribution detection has gained increasing attention in recent years. There are primarily two sets of approaches to OOD detection: post hoc methods and regularization-ban based methods. Post hoc methods involve designing OOD scores at test time, which include confidence-based methods [46).
energy-based scores 668 [09]. gradient- -based scores 10, 26), and distance-based scores [62 91.
Another set of approaches involves leveraging training- time regularization for OOD detection by relying on an additional clean set of semantic OOD data [47 44 [00]. Some recent studies propose utilizing wild mixture data for OOD detection. For example, WOODS [55] considers wild mixture of both unlabeled ID and semantic OOD data. SCONE 66 includes a wild mixture of unlabeled ID, semantic OOD, and covariate OOD data, which are suitable for real-world scenarios. In contrast to previous work, we propose a human-assisted approach for the wild mixture setting and observe that only few hundred human annotations can significantly improve robustness and OOD detection. Unlike 196), which utilizes adaptive human review for OOD detection via a fixed false positive rate threshold, our approach is fundamentally different. We collect informative examples to finetune the model and OOD detector, simultaneously improving OOD detection and generalization.
Noisy binary search. Our algorithm utilizes noisy binary search algorithm to find the threshold where the density difference between covariate OOD and semantic OOD examples flip from negative to positive. In traditional binary search, one simply shrinks the possible interval of the threshold by half based on the observation of either a negative or positive signal. However, in noisy binary search, the observations are inherently noisy with some probabilities. As result, one necessarily needs to maintain a high probability confidence interval of where the threshold may be located. The noisy binary search problem has been primarily studied in combinatorial bandits [18 34 17
15 33] [54]
and agnostic active learning (22 42
23
49 53 56). We primarily utilize a version of the
fix-budget algorithm from [56) it proven be near instance-optimal. In the past, noisy binary
search algorithms have been widely applied in applications such as text classification
84 wireless
networks
88 and training neural networks on in in-distribution data [08 73).
Deep active learning is a vital paradigm in machine learning that emphasizes the selection of the most informative data points for labeling, enabling efficient and effective model training with limited labeled data [107]. There are two main groups of algorithms: uncertainty sampling and diversity sampling. Uncertainty sampling aims identify and select data examples where model confidence is low in order to reduce uncertainty when labeled [35] 28 II (97. Diversity sampling
aims query a batch of diverse examples that are representative of the unlabeled pool for the overall data distribution [85] 39. 38 [12, 20). Recently, some hybrid methods have arisen that consider both uncertainty sampling and diversity sampling, which query batch of informative and diverse examples [3] 4 20 70). Another line of work is deep active learning with class imbalance 21
59 31
Some recent advances consider distribution shifts in the context of deep active learning [05 13
In this work, we consider OOD robustness and tackle the challenging scenario of unlabeled wild distributions, training a robust multi-class classifier and an OOD detector simultancously.
3 Problem Setup
Labeled in-distribution data. Let X' denote the input space and the label space y ; [K] consists of K classes. We have access an initial labeled training set of M examples S.n P. y
Unlabeled wild data. When model is deployed into a wild environment, it encounters unlabeled examples that exhibit various distributional shifts. We consider generalized characterization of the
3

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wild data as: Pwild := (1 Te ] peovariate + +T are non non-negative ratios, unknown the learner.
Pommatiic where 1 - Te Ns. Ne and out
out
P refers to the ID data, which represents the marginal distribution of the initially labeled dataset. peovariate represents the covariate OOD distribution (OOD generalization). The label space remains the same as in the training data, but the input space undergoes shifts in style and domain. peemantic represents the semantic OOD distribution (OOD detection), which encompasses semanties outside the known categories y : [K]. These semantics should not be predicted by the model.
Learning framework. Let fw X RK denote function for the classification task, which predicts the label of an input sample x as T( (x)) == arg max, fw (x). To detect the semantic OOD data, we train a ranking function go X + R with parameter 0. With the ranking function go-
ID
if go (X > À
The threshold value À is typically chosen so that high fraction of ID data is correctly classified.
one can define the OOD detector as a threshold function Do(x; A)
OOD ge S
Learning goal. We aim to evaluate our model based on the following measure- ments: (1) ID-Acc(fw)
(xxx)>>xxx (1( (x)) (2) OOD-Aco(fw) := Ex- (1(9(()) =IN)), where 1. is the indicator function. These metrics collectively assess ID generalization (ID-Acc), OOD generalization (OOD-Acc), and OOD detection performance (FPR), respectively.
(xx.))-roooteepp Pyne (I v3) (3) FPR(go)
Novel Human-Assisted Learning Framework. In addition to the initial labeled training set Sin. a learning algorithm also given a small budget of B examples for human labeling. Before the annotation starts, we assume access to a set of wild examples Swild {x; ~ W / i=1 where their corresponding labels { are unknown. For each example x E Swild chosen by the algorithm, human assistant provides a ground truth label y. Here, y DOD if x is semantic OOD, Otherwise, 7 E [K] represents the class category of X when it is ID or covariate OOD. We let Shuman = {(x, Dt } denote the set of annotated wild examples. At last, neural networks fa and go are trained on S.n Shuman- The objective of the learning algorithm is to choose and label Shuman so that the performances of So and go are optimized (see learning goal above for metrics).
4 Methodology
In this section, we begin by identifying a good labeling region subset of wild examples that can significantly boost the OOD generalization and detection performances if labeled. We first present five straightforward yet novel baseline labeling regions for hussanassisttd OOD learning in Section 4.1 In Section 4.2 to address their limitations, we propose a significantly more effective labeling region, termed the maximum disambiguation region. This labeling region is an interval where the densities of the covariate and semantic OOD scores are roughly equal. We describe the details of the learning algorithm AHA (Adaptive -luman-Assssted OOD learning) in Section (.3 effectively identify and label examples in this region. Lastly in Section 4.4 we discuss the training objective we used incorporate the human feedback for OOD learning.
4.1 Baseline Labeling Regions
Table 2: Practical labeling strategies, such as selecting the top-k most OOD examples and 95% true positive rate (TPR), display worse performance compared to AHA sampling. For experiments, we set a budget of & = 500. We train on CIFAR-10 the ID dataset, using wild data with 0.4 (CIFAR- and = 0.3 (Texture).
The OOD score measured using the energy score.
Labeling Regions
OOD Acc.t
ID Acc.t
FPR.
AUROCI
#ID
#Covariate OOD
# #Semantic OOD
OOD
94.67
7.51
408
Onele
Least Semsnntic OOD
80.84
94 88
27. 10
86.00
325
127
48
Mixed Region
83.12
94 84
9.68
95.88
177
79
244
Maximum Disambignation Region (ours)
94.54
4.81
Poocicall
Top-1: Must Examples Region
85.44
94.55
8.47
96.40
87
413
Near- Boundary Region
87.55
94.77
9.50
95.73
67
263
170
(ours)
94.75
4.69
As shown in Figure D
using given OOD detection scoring function y (we employ the energy score for our case study; see the appendix for detailed description of different scoring function choices g). we can order the wild examples Swid from the least to the most likely of being OOD.

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Let Whild Siid and wild denote the sets of ID, covariate OOD and semantic OOD data in wild Swild respectively. When collecting human feedback, the ideal outcome is to label examples that best separate II) and covariate OOD examples from the semantic OOD ones. This may be achieved by labeling the highest score covariate OOD examples and the lowest score semantic OOD examples. Let Acoaariate maaxxSSaa Saare g(x) and Asemantic == min Ses g(x) denote the scores of the most covariate and the least semantic OOD examples. For analysis purposes, we propose the following three oracular labeling regions with a labeling budget of k:
Mast covariate OOD: Label the top-k OOD score examples from {x E Swild g(x) S Acovariate }.
Least semantic OOD: Label the bottom-k OOD score examples from {x E Swild g(x) Z
Asemantic
Mixture of the two: Allocate half of the budget to most covariate OOD and the remaining half
K 2 to least semantic OOD, combining the two subsets.
In practice, since Aavariiae and Asemantic are unknown, one may opt for the following two surrogate practical labeling regions:
Top-k most OOD examples: As a surrogate to the most covariate OOD labeling region, we label
the top-k OOD score examples from Swild (see Figure ! (a)).
Near-boundary examples: As a surrogate to the least semantic OOD labeling region, we label k examples closest to the 95% TPR threshold from both sides (see Figure 1 (b)). We choose the
threshold based on the labeled II) data Sin which captures a substantial fraction of ID examples (e.g., 95%), and is commonly defined as the ID vs OOD boundary in OOD detection literature.
Limitations in OOD Learning Performance of Baseline Labeling Regions. We conducted a case study on the five novel baseline labeling regions listed in Table 2 Most covariate OOD region targets selecting covariate OOD with the highest scores, it performs poorly in wild settings. Most selected examples turn out to be semantic OOD near Acovariate which does not aid in OOD generalization as expected. Similarly, the oracle Least semantic OOD region aims to identify semantic OOD examples with the lowest scores, and mostly ends up labeling ID and covariate OOD examples near Aseaanic- The Mixed range achieves performance somewhere in between the two. We observe a similar phenomenon for the practical Top- most examples region and Near-boundary region. The above labeling regions are not as effective one might hope. This is primarily due to the dominant number of the other types of data around the most covariate OOD and least semantic OOD examples, which are not informative. This motivates to label examples where the density of the two types of OOD examples roughly equalizes- maximum disambiguation region. Empirically, as shown in Table we observe that labeling within this region can significantly improve overall performance in both oracle and practical settings.
Although our proposed novel oracle
4.2 Maximum Disambiguation Region
In this section, we formally introduce the maximum disambiguation region (see Figure [c)), centered around the maximum ambiguity threshold. While we hope to find the threshold where the densities of semantic and covariate OOD examples equalize, it is impossible to distinguish between covariate OOD examples from ID examples based on human labels. Therefore, we formally define the threshold as the OOD score where the weighted density of semantic OOD examples is equal to that of covariate OOD and ID examples combined.
Concretely, given the OOD scoring function we let Poovariate (4) be the probability density of gxx) when x is drawn from the covariate OOD distribution. That is, ft Peovariate (v )dv is the probability that x drawn from the covariate OOD distribution has a score less than or equal to j.. Similarly, we define Pmn and Psemantic as the probability densities of gxx) when x is drawn from ID and semantic OOD distributions respectively. Recall Tee and π. are the prior probabilities of x coming from the covariate and semantic OOD distributions, we define the maximum ambiguity threshold as follows. Definition 1 (Maximum Ambiguity Threshold). Given the OOD scoring for all wild data points, we define the maximum ambiguity threshold as the CDF of the two categories of examples is maximized:
1." - + TePeovariote (v)) - -TaPommanicl (v)dv.
A_ arg max HER
(1)
Ties are broken by choosing the je value closest to the median of the OOD scores of the wild examples. Note that under benign continuity assumptions, we necessarily have (1 Th πz) Pu (X*) +
ePeovatiate (X*) (X*)
TePsemantic (X*)), where the weighted densities of the two distributions equalize.
5

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Through a different lens, the threshold Xa also corresponds to where the current OOD detector is most uncertain about its prediction. fact, when we label around this threshold, we make the maximum number of corrections to the OOD detector's predictions, correcting at least half of the examples to their appropriate categories.
Reduction to noisy binary search. At the essence, the above is noisy binary search problem. When labeling an examples with OOD score g(x), the outcome is Bernoulli-iike random variable. Specifically, one observes a class label y E [K] with probability Pio (v) + Peovariate (V), and
an y = DOD label with probability Psemanti (v). When given a labeled set S {(x,, Dc) De] of size n, by finite sample approximation, equation ( can be further derived as
max [" ((1 - n.. TT) (V) + Te TePPewarinte - T. "ssemmmntic (v)dv
(2)
HER
max {y. * DOD (x4, 9)) (xx) s - Hy 00D: (x, Ui) E gx(()) S [1]l NER
(3)
4.3 Algorithm
Our algorithm AHA consists of two main steps: (1) We propose identifying the maximum ambiguity threshold by leveraging an aff-the-shell adaptive labeling algorithm [56). This threshold determined by equation 2 where the cumulative number of ID and covariate OOD examples most dominate that of semantic OOD examples. (2) We then annotate an equal number of examples on both sides of this identified maximum ambiguity threshold, establishing the maximum disambiguation region.
Specifically, shown in Algorithm T AHA starts by initializing an empty set for labeled examples and a broad confidence interval for the maximum ambiguity threshold. During the first phase, the algorithm iteratively and adaptively labels more examples. Over the annotation period, our algorithm maintains a confidence interval [,, [i] with high probability, ensuring that the maximum ambiguity threshold A [p. j4] lies within this interval with high probability. During each iteration of the first phase, we uniformly random label an example within this confidence interval. Upon obtaining the label, we update the confidence interval using subprocedure called ConfUpdate. This subprocedure shrinks the interval based on the labeled examples, ensuring converges to an accurate threshold over time with statistical guarantees. The detailed implementations of ConfUpdatt and its theoretical foundations are discussed in Appendix AA and (56) respectively. Overall, we spend half of our labeling budget during the first phase. During the second phase, we then spend the remaining half of the budget labeling examples around the identified threshold. Finally, the classifier and the OOD detector are trained on the combined set of initially labeled and newly annotated examples.
Algorithm 1 AHA: Adaptive Human Assisted labeling for OOD learning
Input: OOD detector g trained on Sn, wild set of examples Swild = {x,] budget k Initialize: Snuman + 0. confidence interval μt, f + 00, oc
Spend half budget searching for maximum ambiguity threshold
fort= 1,.... 3 do Sample X, uniformly at random from {x E Swild (euiaasSumann S g(x) S n
Ask human for label on X,, observe Tt. and insert the example (X,, Ft into Snuman
Update confidence interval p.p + ConfUpdate( (Summan: Swild 9. 11. P))
end for
Spend half budget labeling around identified threshold
Compute j as an arbitrary solution that reaches the maximum in equation
Label examples Top : {x Swad (Stmman : g(x) < ()::) and Bottom fx E Swatd's S SSuman g(x) > p}:9) and insert them in Spunan
Return: New classifier fw and OOD detector go trained on Si, U Shuman based Section 4.4
4.4 Learning Objective
Let Sc Summan denote the set of annotated covariate examples, and St Suman represent the set of annotated semantic examples from wild data. Our learning framework jointly optimizes two objectives: (1) multi-class classification of examples from Snn and covariate OOD &c S and (2) binary OOD

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detector separating data between Sjn and semantic OOD Suman
The risk formulation is defined as:
w, 0 arg min R. Yinnon (Sw) ta. R.... (ge))
(4)
Multi-class classifier
OOD detector
where is the weighting factor. The first term is optimized using standard cross-entropy loss, and the second term is aimed at explicitly optimizing the level-set based on the model output (threshold at 0):
R Jumen (go) R. (00) + R (go)
(5)
Exes, (x) 0) + Exes
[gga (x) 0).
We replace the 0/1 loss with the binary sigmoid loss as a smooth approximation to the 0/1 loss.
5 Experiments
In this section, we comprehensively evaluate the efficacy of the AHA for OOD generalization and detection. First, we describe the experimental setup in Section [5.1 In Section 5.2 we present the main results and discussion on OOD generalization and detection. Then, we provide ablation studies to further understand the human-assisted OOD learning framework (Section 5.3)
5.1 Experiment Setup
Datasets and evaluation metries. Following the benchmark in literature of [6], we use the CIFAR-
10 [60] as Pin and CIFAR-1O-C [45] with Gaussian additive noise as the |pouvariate for our main experiments. We also provide ablations on other types of covariate OOD data in the Appendix | For semantic OOD data (Psemantic), ), we utilize natural image datasets including SVHN (72), Textures 19
out
Places365 [T3. LSUN-Cron Crop [103]. and
[103]. Additionally, we provide results on the PACS dataset [64] from DomainBed. Large-scale results on the ImageNet dataset can be found
in Appendix F A detailed description of the datasets presented Appendix [D] To compile the wild data, we divide the ID set into 50% labeled as ID (in-distribution) and 50% unlabeled. We then mix unlabeled ID, covariate OOD, and semantic OOD data for our experiments. To simulate wild distribution IP wiild.
adopt the same mixture ratio used in the benchmark of SCONE (6), where Te = 0.5 and म. = 0.1. We also evaluate different wild mixture rates in Section 5.3 For evaluation, we use the collection of metrics defined in Section B] The threshold for the OOD detector is selected based on the ID data when 95% of ID test data points are correctly classified as ID.
Experimental details. For CIFAR experiments, we adopt Wide ResNet [104] with 40 layers and a widen factor of For optimization, we use stochastic gradient descent with Nesterov momentum 27), including a weight decay of 0.0005 and momentum of 0.09. The batch size is set to 128, and the initial learning rate is 0. with cosine learning rate decay. The model initialized with a pre-trained network on CIFAR- 10 and trained for 100 epochs using our objective from Equation 4 with a = 10. We set a default labeling budget of 1000 for the benchmarking results and provide an
Table 3: Main results: comparison with competitive OOD generalization and OOD detection methods on CIFAR-10. *Since all the OOD detection methods the same model trained with the CE loss P.,. they display the same ID and OOD accuracy on CIFAR-IO-C. We report the average and standard error (drr) of our method based on three independent runs.
SVHN CIPAR PL
LSUN-C poweik
CHEAR-IO CO geowee
Texture poemar
CIEAR IBCC pronNO
OGD Acc.1
1D Acc:"
FFR.
AUROCT
OOD Acc.!
ID Avc.1
FPR.
AUROC!
[ODD Asc.
10 Act.
FFR.
AUROC!
000 Jiteciin
OBIN
75
33.35
15.52
75.05
$4.17
Energy
75.05
94.44
03.59
00100
15.05
94,
8.26
98.35
75.05
04.34
52.70
$5.22
Mahainanhis
75.05
94.64
12.80
07 62
75.05
04.54
30:22
94.15
73.05
04.84
15.00
97.33
VIM
75
14.11
21
028
75.05
3.81
83
11511
96.35
29:35
53.71
75.05
75.05
0G0 geoovllzaionn
ERM
75
94.84
35.59
98196
75.05
94.84
8.36
98.25
75.05
94.54
53.79
55.22
Mhxp
72.92
77.02
$7.81
VREA
76.90
91.35
$5.92
01 22
75.00
91.35
$1.50
91.55
00.00
91.35
65.45
$5.46
RQKM
75.71
93.43
51 385
981
75.11
92.95
31.55
95.80
75.71
07.93
$7.18
19.11
SharpTRO
75003
94.11
14.34
79.05
94.91
3.67
95.71
19.03
94.91
42.94
$9.90
OE
37661
0.84
59
93.99
310
99.26
44.71
93.95
Energy 14, cotller)
31.74
M6.32
0.56
99.91
32.55
92.97
2.3
95.93
19.34
94.55
16.43
96.46
WOODDS
K3.76
94.85
2.11
75.50
$5.00
1.00
99.56
13.14
94.09
39.10
$0..5
$4.09
94655
0.68
99
34.58
95.73
10:33
99.02
85.56
03.07
50.91

---

analysis of different labeling budgets 100, 500, 1000, 2000 in Section 5.3 3 In our experiment, the output of go is utilized as the score for OOD detection.
5.2 Main Results and Discussion
Results benchmark for both OOD generalization and detection. Table provides parative analysis of various OOD generalization and detection methods on the CIFAR benchmark, evaluating their performance across different semantic OOD datasets including SVHN, LSUN-C, and Textures. AHA shows significant improvements for both OOD generalization and OOD detection tasks, suggesting robust method for handling OOD scenarios.
Specifically, we compare AHA with three groups of methods: (1) methods developed for OOD gencralization, including IRM (2), GroupDRO [8]] Mixup [T66.. VREx [6]], EQRM [29], and
the more recent SharpDRO [1]; (2) methods tailored for OOD detection, including MSP
46
ODIN 667.. Energy (68], Mahalanobis [62]. ViM 198], KNN 191), and the more recent ASH
25
and (3) methods that are trained with unlabeled data from the wild, including Outlier Exposure 47 Energy-based Regularized Learning (68), WOODS [55], and SCONE [6].
We highlight some key observations: (1) AHA achieves superior performance compared to specifically designed OOD generalization baselines. These baselines struggle to distinguish between II) data and semantic OOD data, leading to poor OOD detection performance. Additionally, our method selects the optimal region and involves human labeling retrain the model using the selected examples, thus leading to better generalization performance compared to other OOD generalization baselines. (2) Our approach achieves superior performance compared to OOD detection baselines. Methods specifically designed for OOD detection, which aim to identify and separate semantic OOD, show suboptimal OOD accuracy. This demonstrates that existing OOD detection baselines struggle with covariate distribution shifts. (3) Compared with strong baselines trained with wild data, AHA consistently outperforms existing learning with wild data baselines. Specifically, our approach surpasses the current state-of-the-arr (SOTA) method, SCONE, by 31.52% terms of FPR95 on the Texture OOD dataset and simultaneously improves the OOD accuracy by 4.95%. This demonstrates the robust. effectiveness of our method for both OOD generalization and detection tasks.
Additional results PACS. Table 4 presents results on the PACS dataset 64 from Do- mainBed [40]. We compare AHA against various common OOD generalization baselines, including
IRM 21. DANN (37. CDANN (66), GroupDRO 181, MTL [14, I-Mixup [OO]) MMD [65].
VREx 661. MLDG (63. ARM [III). I. RSC [50], Mixstyle [116], ERM (95], CORAL [89], Sag-
Net 771. SelfReg (57), GVRT 669, VNE [58] and the most recent bascline HYPO I7]. Our method achieves an average accuracy of 92.7%, outperforming these OOD generalization baselines.
Table 4: Comparison with domain generalization methods the PACS benchmark. We followed the same
Table 5: Impact of sampling scores with our selection strategy. We use budget & = 1000 for all methods. We train on CIFAR 10 as ID, using wild data with
validation experimental protocol as in (64). All methods are trained on ResNet- 50. The model selection is based on training domain validation set.
0.5 (CIFAR-10 10-C) and
0.1 (Texture).
Sampling score
0OD Acc:t
ID Acc.t
FPR.
AUROCT
Random
89.22
9.45
Algorithm
Art
Cartoon
Photo
Sketch
Average
Least confidence
90.08
94.40
5.29
97.94
2
76.1
Entropy 90.10
DANN BY
86.4
77.4
97.3
73.5
83.7
Margin
94.55
4.15
98.53
CDANN (6)
GroupDRO
84.6
83.5
75.5
79.1
96.7
96.8
73.5
78.3
82.6
$4.4
Gradient-bused
Energy
89.58
94.73
6.37
5.63
97.26
MTL L4
$7.5
77.1
96.4
77.3
$4.6
Table 6: Ablation on labeling budget k. We train
LMixup
86.1
78.9
97.6
75.8
84.6
MMD 65
86.1
79.4
96.6
76.5
84.7
CIFAR-10 as ID, using wild data with me 0.4
VREx 61
$6.0
79. 1
96.9
77.7
$4.9
(CIFAR-1O-C) and F. 0.3 (Texture).
MLDG
[64]
76.6
RSC 50
ARM
86.8
85.4
79.7
76.8
97.44
97.66
78.2
79.3
85.1
85,2
Budget
Method
OOD Acc.t
ID Acc.t
FPR.
AUROC!
Mixstyle
78.5
100
Top-k
79.77
94,89
17.55
91,98
ERM 95
$4.7
80.8
97.2
79.3
85,5
SagNet
CORAL 89
88.3
50.0
97.5
97.1
78.8
86.2
500
AHA (Ours)
Top-k
88.30
85.44
94,75
94.55
4.69
8.47
98.22
96.40
SelfReg 57
87.9
79.4
96.8
78.3
85.6
GVRT [M
VNE
88.6
79.9
78.4
98.2
96.7
75.7
82.3
85.1
86.9
Top-k
(Ours)
88.32
94.51
97.62
HYPO 7
90.5
84.6
97.7
83.2
$9.0
2000
AILA (Ours)
Top-k
90.46
89.87
94.47
94.41
2.04
2.64
99.17
99.05
8

---

5.3 Ablation Studies
Effect of different scores. Different OOD scores play erucial role in identifying various distri- butions and impacting the selection process. To evaluate the effectiveness of different OOD scores within our framework, we conducted an ablation study (see T5b Detailed descriptions of the
different OOD scores can be found in Appendix C The scores include least-confidence (97 44] en-
tropy 977, margin [80], energy score [68], gradient-based [26]. We also compared our approach with random sampling, which serves as straightforward baseline method involving the random selection of l examples to query. We observe that AHA consistently achieves superior performance when combined with various sampling scores for OOD generalization and detection, and it consistently outperforms the random sampling baseline, The gradient-base score demonstrates the best overall performance in terms of OOD accuracy and FPR. This also shows that AHA can be easily integrated with existing sampling scores.
Effect on different labeling budgets k. In Table 6 we provide ablations on different labeling budgets from 100, 500, 1000, 2000. We observe that both OOD generalization and detection performance improve with an increasing labeling budget. For instance, our method's ' OOD accuracy increased from 79.77% to 90.46% when the budget increased from 100 to 2000. Simultaneously, the TPR decreased from 17.55% to 2.04%, which also indicates significant improvement in OOD detection performance. Moreover, AHA consistently outperforms the practical top-k OOD example sampling strategy across different labeling budgets.
5.4 Qualitative Analysis
eew
3.00
(a) T-SNE of ERM
(b) T-SNE of Ours
(c) Scores of ERM
(d) Scores of Ours
Figure 2: (a)-(b): T-SNE visualization of the image embeddings for ERM vs. AHA (ours). (c)-(d) Score distributions for ERM AHA (ours). Different colors represent the different types of test data: CIFAR-10 as PLa (blue), CIFAR-IO-C as ppoovariate (green), and Textures as poemantic (gray).
Visualization of feature embeddings. Figure (a) and (b) present feature embedding visualizations 93 on the test data. The blue points represent the ID test data (CIFAR-10) 10), green
using t-SNE points represent OOD test examples from CIFAR-10-C, and gray points from the Texture dataset. We observe that (1) the embeddings of the ID data P.. (CIFAR-10) and the covariate shift OOD data ppooaarite (CIFAR-10-C) are more closely aligned, and (2) the embeddings of the semantic shift OOD data provariate (Texture) are better separated from the ID and covariate shift OOD data using our method. This contributes to enhanced OOD generalization and OOD detection performance.
Visualization of OOD score distributions. Figure (c) and (d) visualize the score distributions using kernel density estimation (KDE) for the baseline and our method. The OOD score distributions between the ID data (In,)) and the semantic OOD data (Eeemmmiic) are more separated using our method. This separation represents an improvement in OOD detection performance, demonstrating the effectiveness of AHA in identifying semantic OOD data.
Conclusion
In this study, we introduce the first human-assisted framework designed to simultaneously address OOD generalization and OOD detection by leveraging wild data. We propose a novel labeling strategy that selects the maximum disambiguation region, strategically utilizing human labels to maximize model performance amid covariate and semantic shifts. Extensive experiments demonstrate that AHA effectively enhances both OOD generalization and detection performance. This research establishes solid foundation for further advancements in OOD learning within dynamic environments characterized by heterogeneous data shifts.

---

Acknowledgement
This work has been supported in part by NSF Award 2112471.
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AHA: Human-Assisted Out-of-Distribution Generalization and Detection (Appendix)
A ConfUpdate: Shrinking Confidence Interval for Labeling
Given the current interval [M, /il, the OOD scoring function g and the wild dataset Swild. we can denote Spp.aj as the set of examples S [. == (x Swidd 14 S g(x) (.). The goal of ConfUpdate is to shrink the two ends of this interval of examples so that its size shrink by factor of == (V/|Swaal-
This way, after labeling examples, the confidence interval would only contain single example.
We let the examples (I)),...; (m) denote ordered list of examples in ((m, based on the OOD scoring function g. Note (s the confidence interval is shrunk by finding T, J [m] where / < J such that:
I,J= arg min (ax( where 1.3-jimm
[(s) (Sa:X(()) ESuam
Σ 1((r) + 0DD)
Σ
(((()) = 00D)
+S ES
Here, Shuman is the labeled set of examples from input to the algorithm. Each example in this set. therefore has corresponding label y. The loss Z(s) is empirical estimate of the loss in equation Intuitively, this shrinking procedure is choosing the fixed-size subset interval that will result in the
lowest empirical loss estimate based current labeled examples.
Finally, we return g(x1), g(x)) as the new confidence interval for μ. p.
B
Main Notations and Their Descriptions
Table 7: Main notations and their descriptions.
Notation
Description
Spaces
X' y
the input space and the label space.
Distributions
P wilds Iiim
data distribution for wild data and ID data.
poovariate IP semantic P
data distribution for covariate-shifted OOD data. data distribution for semantic-shifted OOD data. the joint data distribution for ID data.
Data and Models
S Swild Shuman human S fwr and go
labeled ID data and unlabeled wild data labeled data
semantic and covariate OOD in the labeled data Snuman
predictor on labeled in-distribution and binary predictor for OOD detection label for ID classification
Ux n. m. k
Predicted one-hot label for input x size of Sin size of Swild, labeling budget
Algorithm and Labeling Region
Acovariate
highest OOD score of covariate OOD examples
Asemantic
lowest OOD score of semantic OOD examples
Xa
maximum disambiguity threshold high probability confidence set of possible location of the maximum ambiguity threshold
μ, f
17

---

C Description of Different OOD Scores functions g
In this section, we provide a detailed description of the different scoring function choices for g. which have been shown to work well for detecting semantic OOD images. Our proposed good labeling region method is orthogonal to post hoc OOD scores, allowing to be integrated with various OOD score functions.
is simple baseline OOD score that probabilities from softmax distributions. This score focuses on instances where the model's predictions are least certain.
MSP 46 97
Margin 18 refers to the multiclass margin value for each point, specifically calculating the discrep- ancy between the posterior probabilities of the two most likely labels. Most OOD examples have closely matched posterior probabilities, indicating minimal difference between them.
Entropy score [46 97 quantifies how evenly spread the model's probabilistic predictions are among all K classes. We calculate the entropy within the predictive class probability distribution of each example.
Energy score (68) identifies data points based on an energy score, which is theoretically aligned with the probability density of the inputs. This score evaluates the likelihood of each input belonging the known distribution, providing robust measure for distinguishing between ID and OOD examples.
Gradient-based score 226 leverages the gradients of the loss function differentiate between ID and OOD data. This gradient-base based filtering score provides a robust mechanism for identifying OOD in the wild by leveraging the inherent differences in gradient behaviors between ID and OOD data.
D Detailed Description of Datasets
In this section, we provide a detailed description of the datasets used in this work.
CIFAR-10 (60) includes 60, 000 color images in 10 different classes, with 6,000 images per class. This is widely used benchmark in machine learning and computer vision. The training set consists of 50, 000 images, while the test set comprises 10, 000 images.
The corruption types include Gaussian noise, defocus blur, glass blur, impulse noise, shot noise, snow, zoom blur, brightness, elastic transform, contrast, fog, forest, Gaussian blur, jpeg, motion blur, pixelate, saturate, spatter, and speckle noise.
CIFAR-10-C is generated based on the previous leterature 45
ImageNet-
100 is a dataset composed of 100 categories randomly sampled from the ImageNet-1 dataset (24] The classes included in ImageNet- 100 4098015:001498041, n01 501514859, n01582220,
n01608432, n01616318, n01687978, n01776313, n01 n01806567, n01833805, 801882714, n01910747, a01944390,
n01985128, m02007558, n02071294, n02085620, n02114855, n02123045, n02128385, n02129165, n02129604,
n02165456, n02190166 n02219486, n02226429, n02279972, n02317335, n02326432, n02342885, n02363005,
n02391049, n02395406, m02403003, n02422699, n02442845, n02444819, n02480855, n02510455, n02640242.
102672831, m02687172, m02701002, n02730930, n02769748, n02782093, n02787622, n02793495, a02799071,
m02802426, n02814860, a02840245, n02906734, m02948072, n02980441, n02999410, n03014705, n03028079.
m03032252, n03125729, nO3160309, n03179701, m03220513, n03249569, n03291819, n03384352, n03388043,
n03450230, n03481172, mO3594734, n03594945, n03627232, m03642806, n03649909, n03661043, n03676483,
m03724870, m03733281, nO3759954, n03761084, m03773504, n03804744, n03916031, n03938244, n04004767,
n04026417, n04090263, mO4133789, no4153751, n04296562, m04330267, n04371774, n04404412, n04465501,
n04485082, n04507155, n04536866. n04579432. no4606251, n07714990, n07745940.
LSUN [03
a large image dataset with categories labeled using deep learning with humans in the loop. LSUN-C C is a cropped version of L.SU,, and LSUN-R is a resized version of the LSUN.
Textures [19 contains images of patterns and textures. The subset we use for the OOD detection task has no overlapping categories with the CIFAR dataset.
SVHN 72 is natural image dataset containing house numbers from street-level photos, cropped from Street View images. This dataset includes classes, with 73, 257 training examples and 26, 032 testing examples.
18

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Table 8: Additional results. Comparison with competitive OOD detection and OOD generalization
methods on CIFAR-1O. For experiments using Fwild, we use T = 0.5, Re 0.1. For each semantic OOD dataset, we create corresponding wild mixture distribution wild (1 nT Te x
pseeaatic a nut Te P for training. We report the average and standard error (#:x) of our method
based three independent
Places365 For
CIFAR-IO-C Pourtn
LSUN-R pomanic CIFAR- 10-C ponetr
OOD Acc.
ID Ace.t
FPR.
AUROCT
OOD Accit
ID Acc.f
FPR.
AUROCT
00D detection
MSP
75.05
84.49
94.84
91.37
ODIN
75.05
94.84
57.40
84.49
75.05
94.84
26.62
94.57
Energy
40.14
94.84
94.24
Mahalanobis
75.05
94.84
68.57
84.61
75.05
94.84
42,62
93.23
VIM
75.05
94.84
21.95
95.48
75.05
94.84
36.80
93.37
KNN
75.05
94.84
42.67
91.07
75.05
94.84
29.75
94.60
ASH
75.05
94.84
44.07
88.84
75.05
94.84
22.07
95.61
gonevalization
ERM
75.05
94.84
40.14
89.89
75.05
94.84
27.58
94,24
Mixup
93.30
IRM
77.92
90.85
53,79
88. 15
77.92
90.85
34,50
94.54
VREX
76.90
91.35
56.13
87.45
76.90
91.35
44.20
92.55
EQRM
75.71
92.93
$1.00
88.61
75.71
92.93
31.23
94.94
SharpDRO
79.03
94.91
34.64
91.96
79.03
94.91
13.27
97.44
Learning PusM
OE
35.98
94.75
27.02
94.57
46.89
94.07
0.70
99.78
Energy (w/ outlier)
19.86
23.89
Woods
54.58
94.88
30.48
93.28
78.75
95.01
0.60
99.87
Scone
85.21
94.59
37.56
90.90
80.31
94.97
99.79
AHA (Ours)
88.93
94.30
11,88
95.60.0.11
91.08
0.07 0.07 91.41g0.00
99.98
Places365 [133] is a large-scale image dataset comprising scene photographs. The dataset is divided
into several subsets to facilitate the training and evaluation of scene classification. It highly diverse and offers extensive coverage of various scene types.
[94] is challenging real-world collection featuring species captured in diverse situations. It comprises 13 super-categories and 5,089 sub-categories. For our experiment, we use the subset provided by 48). which contains 1 11 plant classes with no overlap with the IMAGENET-IK
iNaturalist
categories 24
PACS [64] is commonly used OOD generalization dataset from DomainBed 440 It includes four domains with different image styles: photo, art painting, cartoon, and sketch, and it covers seven categories. It is created by intersecting the classes found in Caltech256 (Photo), Sketchy (Photo, Sketch) 833. TU-Berlin (Sketch) [30], and Google Images (Art painting, Cartoon, Photo). This dataset consists of 9,991 examples with resolution of 224 x 224 pixels.
Data split details for OOD datasets and composing wild mixture data. Following previous work [55 b). we use different data splitting strategies for standard and OOD datasets. For datasets with a standard train-test split, such as SVHN, we use the original test split for evaluation. For other OOD datasets, we allocate 70% of the data create the wild mixture training data and the mixture validation dataset, while the remaining 30% is reserved for test-time evaluation. Within the training/validation split, 70% of the data is used for training, and the remaining 30% is used for validation.
E
Results of Additional OOD Datasets
Table g presents the main results on additional OOD datasets, including Places365 [13 and LSUN-
Resize 103 Our proposed approach achieves strong performance OOD generalization and OOD
detection on these datasets. We highlight some observations: (1) We compare our method with
post-hoc OOD detection methods such as MSP (4), ODIN [67] Energy [68], Mahalanobis [62].
ViM 198). KNN 191), and the most recent method ASH 25]. These approaches are all based
on model trained with cross-entropy loss, which demonstrates suboptimal OOD generalization performance. (2) We compare our method with OOD generalization approaches, including IRM ( GroupDRO [8], Mixup [166), VREx (T), FQRM [29), and the most recent method SharpDRO 51).
Our approach achieves improved performance compared to these OOD generalization baselines. (3)
19

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Additionally, we compare our method with learning from Piild OOD baselines, such as OE 47 Energy [68]. WOODS [55], and SCONE 6 Our approach achieves strong performance on both OOD generalization and detection accuracy, demonstrating the effectiveness of our human-assisted OOD learning framework for both OOD generalization and OOD detection.
Table 9; Results ImageNet- 100. We ImageNet-100 100 ID, and iNaturalist for Premuate
Method
OOD Acc."
ID Acc.f
FPR95]
AUROCT
WOODS
5S
44.46
86.49
10.50
98.22
SCONE G
65.34
87.64
27.13
95.66
AHA (Ours)
72.74
86.02
2.55
99.35
F Results on ImageNet-100
We provide additional large-scale results on the ImageNet benchmark. We use ImageNet- as the ID data iI)),), with labels provided Appendix D For the semantic-shifted OOD data, we use the
high-resolution natural images from iNaturalist [94] with the same subset as employed in the MOS
approach [48]. We fine-tune ResNet-34 model 43 (pre-trained on ImageNet) for 100 epochs, using
that AHA can improve OOD detection performance compared to WOODS and SCONE, achieving better FPR95 and AUROC.
an initial learning rate of 0.01 and batch size of 64. Table
G Effect on different mixing ratios of wild data.
Table 10: Ablation on different mixing ratios of wild data. The labeling budget is & = 1000. The OOD score used is the energy score. We train on CIFAR-10 as ID, using wild data with Te (CIFAR-10-C) and T. (Texture).
Ratios
Method
OOD Acc.t
ID Ace.f
FPR.
AUROC1
We 0.4,
Top-k
88.32
94.51
5.41
97.62
T, 0.3
(Ours)
94.50
3.19
98.83
Te 0.5.
Top-k
88.27
94.60
5.65
97.75
n, 0.2
AHA (Ours)
88.72
94.38
4.75
98.48
Te 0.5.
Top-k
88.90
94.51
7.45
96.79
T, 0.1
AHA (Ours)
89.58
94.73
6.37
97.26
n. 0.6.
Top-k
94,50
8.11
76.77
0.1
AHA (Ours)
89.31
94.59
7.33
96.88
In Table 10 provide ablation study different fractions of covariate OOD me and fractions of semantic OOD data тe within the wild distribution Palld- We focus primarily on evaluations where n. 0, ne * 0. and 1 1 ne л. # O, of distributions in the wild. We observe that OOD generalization performance for top-k sampling generally increases with higher fraction of covariate OOD and lower fraction of semantic OOD, since more covariate OOD are selected and annotated in top-k sampling. Additionally, AHA consistently achieve better performance compared top-k for both OOD generalization and detection. The improvement is more significant with a larger fraction of semantic OOD data.
problem uniquely introduces these three types
H Additional Visualization Results for Real Wild Data
We provide additional visualization on the OOD score distribution for various datasets Figure
I Hyperparameter Analysis
Table II provides an ablation study on varying the hyperparameter a, which balances the weight between the two loss terms. We observe that the performance is strong and remains insensitive across a wide range of a values.
J Results of Different Covariate Data Types
provide additional ablation studies of different covariate shifts (see Table 12.. evaluate AHA under 19 different common corruptions, including Gaussian noise, impulse noise, brightness, zoom
20

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(a) Scores of Pin (CIFAR- (b) Scores of P. (CIFAR- (c) Scores of In (CIFAR- (d) Scores of Pin (CIFAR-
10), Pouraniee (Gaussian), 10), Por (Gaussian), 10), P peosaattte (Impulsc 10), peemantic (SVHN).
out (Impulsc
out (Places),
noisc), out
(SVHN). noise),
out (Places).
Figure 3: (a)-(c) Score distributions for the real wild data. Different colors represent the different types of test data: CIFAR-IO as Pm (blue), CIFAR-I0 C--AR-O--CAA peovariate (green), and Textures as Foul
Iptemamic (gray).
Foul
Table 11: Ablation study on the effect of loss weight a. The sampling strategy is top-k sampling, with a budget of 1000. We train on CIFAR-I0 as ID, using wild data with n. 0.5 (CIFAR-10-C) and n 0.1 (Texture).
Balancing weights
OOD Acc.t
ID Acc.1
FPR.
AUROCT
90.01
94.53
3.25
98 98
a=3.0
89.80
94.51
3.19
98.83
a=5.0
89.73
94.53
3.19
89.73
a=7.0
89.66
94.55
3.25
98.99
9=9.0
89,59
94.51
3.19
99.05
blur, and others. These covariate shifts are generated based on previous literature 45) Our approach consistant performance under different covariate shifts and achieves enhanced OOD generalization and OOD detection performance.
K Software and Hardware
Our framework was implemented using PyTorch 2.0.1. Experiments are performed using Tesla V100.
L Broader Impact and Limitations
By improving OOD generalization and detection for machine learning models, this work can help increase the robustness and reliability of deployed AI systems across many real-world applications. Failing to properly handle distribution shifts a key vulnerability of current AI that can lead to errors, discriminatory behavior, and safety risks. Our human-assistedd framework leverages strategic data labeling approach to cost-effectively boost OOD performance. This could benefit high-stakes domains like medical diagnosis, autonomous vehicles, financial services, and content moderation systems where distribution shifts are common and errors can have severe consequences.
At the same time, there are potential negative impacts to consider. While human labeling can improve model performance, it also introduces privacy risks if the labeling process exposes sensitive data, There are also potential risks of labeling bias or low quality labels degrading rather than enhancing model behavior. From an ethical AI perspective, increasing the capability and deployment of highly capable AI systems carries inherent societal risks that must be weighed against the benefits.
To mitigate these risks, we advocate for implementing robust data governance policies, secure data pipelines, anti-bias monitoring, and careful vetting of crowdsourced labels. We also encourage developing complementary approaches make models more inherently robust distribution shifts, rather than relying solely on human labeling which can be costly and difficult to scale. Overall, we believe the potential positive impacts of safer, more reliable AI outweigh the risks appropriate safeguards are put in place. But we must remain vigilant about responsibly developing and deploying AI capabilities that are beneficial to society.
Limitations. While our human-assisted framework shows promising results for improving OOD generalization and detection, it still requires some human annotation effort. Further reducing the required labeling cost can be a key focus for future work.
21

---

Table 12: Ablations the different covariate shifts. We train CIFAR-10 ID, using CIFAR-10-C
as pood and SVHN as Paoaaatti (with 7e 0.5 and nT = 0.1).
Covariate shift type
Method
OOD Acc.f
ID Acc."
FPR.
AUROCT
WOODS
52,76
94.86
2.11
99.52
Gaussiun noise
SCONE
84.69
94.65
10.86
97.84
AHA (Ours)
89.14
94.64
0.09
99.97
WOODS
94.76
94.99
0.88
99.83
Defocus hlur
SCONE
94.86
94.92
11.19
97.81
AHA (Ours)
94.74
94.70
0,05
99.98
WOODS
38.22
94,90
1,63
99.71
Frosted glass blur
SCONE
69.32
94.49
12,80
97.51
(Ours)
80.49
94.48
0.20
99.93
WOODS
70.24
94.87
2.47
Impulse noise
SCONE
87.97
94.82
9.70
97.98
AHA (Ours)
92.17
94.71
0.07
99.97
WOODS
70.09
94.93
3.73
99.26
Shot noise
SCONE
88.62
94.68
10.74
97.85
AHA (Ours)
91.87
94,56
0.07
99.97
WOODS
88.10
95.00
2.42
99.54
SCONE
90.85
AHA (Ours)
92.85
94.72
0.07
99.97
WOODS
69.15
94.86
0.38
99.91
Zoom
SCONE
90.87
94.89
7.72
98.54
AHA (Ours)
92.04
94.53
0.07
99.98
WOODS
94.86
94,98
1,24
99.77
Brightness
SCONE
94.93
94.97
1.41
99.74
(Ours)
94.77
94.77
WOODS
87.89
Elastic transform
SCONE
91.01
94,88
8.77
98.32
HHA (Ours)
90.99
94.74
0.07
99.97
WOODS
94.37
94.94
1.06
99.80
Contrast
SCONE
94.40
94.98
1.30
99.77
AHA (Ours)
94.34
94.66
0.06
99.98
WOODS
94.69
95.01
1.06
99.80
Fog
SCONE
94.71
95.00
1,35
99.76
AHA (Ours)
94.67
94.72
0.07
99.98
WOODS
87.25
94,97
2.35
99.55
Frost
SCONE
91.94
94.85
10.08
98.03
AHA (Ours)
92.23
94.73
0.05
99.98
WOODS
94.78
94.98
0.87
99.83
Gaussian blur
SCONE
94.76
94.86
3.14
99.39
(Ours)
94.58
WOODS
84.35
94.96
1.73
99.68
Jpeg
SCONE
87.87
94,90
8.14
98.49
AHA (Ours)
89.24
94.53
0.05
99.98
WOODS
82.54
94.79
0.47
99.88
Motion blur
SCONE
91.95
94,90
9.15
98.18
AHA (Ours)
92.58
94.59
0.05
99.98
WOODS
91.56
94.91
1.82
99.66
Pixelate
SCONE
92.08
AHA (Ours)
93,34
94,61
0.05
99.98
WOODS
92.45
95.03
1.26
99.77
Saturate
SCONE
93.38
94.92
10.27
97.88
AHA (Ours)
93.40
94.79
0.06
99.98
WOODS
92.38
94,98
1.94
99.64
Spatter
SCONE
92.78
94.98
1.94
99.64
AHA (Ours)
93.68
94.73
0.07
99.97
WOODS
72.31
94.94
3.51
99.30
Speckle noise
SCONE
88,51
94,83
11.05
97.82
AHA (Ours)
92.00
94.73
0.08
99.97
WOODS
81.72
94.94
1.65
99.68
Average
SCONE
90.29
94.86
7.62
98.50
AHA (Ours)
92.06
94.67
0.07
99,97
22

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NeurIPS Paper Checklist
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Reviews:
[{"review_id": "EmP94n4W3E", "reviewer": "Reviewer_Eoxk", "summary": "This paper introduces a novel, integrated approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild. Extensive experiments validate the efficacy of AHA.", "questions": "see weakness", "limitations": "see weakness", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. this paper is well written and easy to follow\n2. good visualization and extensive experiments\n3. Maximum Disambiguation Region and reduction to noisy binary search are new to me", "weaknesses": "1. there exsits a strong assumption that the weighted densities of semantic and covariance ood should equalize\n2.  what is difference between active learning and the proposed Human-Assisted Learning \n3. what is the time used for noisy binary search\n4. whether the lamda searched on the one dataset can be transfer to another dataset", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your positive feedback and comments. We address each comment below in detail.\n\n> *W1. There exists a strong assumption that the weighted densities of semantic and covariance ood should equalize*\n\nThank 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).\n\nSpecifically, our maximum ambiguity threshold formulation in Eq. (1): \n\n$\\lambda_* = argmax_{\\mu \\in R} \\int_0^\\mu ((1-\\pi_c -\\pi_s)p_{\\text{in}}(\\nu) + \\pi_c p_{\\text{covariate}}(\\nu)) - \\pi_s p_{\\text{semantic}}(\\nu) d\\nu$\n\nindicates 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.\n\n\n> *W2. What is difference between active learning and the proposed Human-Assisted Learning*\n\nHuman 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.\n\nWe 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.\n\n\n> *W3. What is the time used for noisy binary search*\n\nThe average time for the noisy binary search process is 0.3232 milliseconds per point on Tesla V100 GPU.\n\n> *W4. Whether the lamda searched on the one dataset can be transfer to another dataset*\n\nThe 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.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CIJwBtrnjz", "reviewer": "Reviewer_Rqpf", "summary": "This paper proposes to address both out-of-distribution detection and generalization within one joint framework under human-assistance. The proposed method first utilizes a noisy binary search algorithm to identify the most informative samples to be labeled. Then, it continues to annotate these samples with human feedback. The authors conduct experiments on CIFAR and PACS to evaluate the proposed method.", "questions": "Please refer to weaknesses and address my concerns.", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed method can handle OOD detection and generalization at the same time, which is impactful to both these two individual research areas. \n- Covariat-shifts and semantic shifts are both inevitable in real-world applications. Thus the proposed method is straightforward and well-motivated.\n- Most parts of this paper are well presented with good visualization.", "weaknesses": "- AHA may only work under a rather strict assumption. Compared to outlier exposure, AHA needs to access the real test data distribution $S_{wild}$ to selectively label some samples. While outlier exposure does not need such an assumption. Many previous OOD detection methods can only access training data distribution and an auxiliary OOD data distribution (noted that such auxiliary OOD has no overlapped samples with test-time OOD data in common settings). Thus I think AHA may only work under a more strict assumption (i.e., the test data distribution $S_{wild}$ is accessible) than previous outlier exposure.\n- I generally believe enhancing OOD detection and generalization with human feedback is laborious. I am aware the proposed method can get good performance with hundreds or thousands of labeled samples in many cases. However, such human feedback still seems laborious to me. For example, in CIFAR experiments, the images are only 32*32. Thus I think it would take a lot of time to label such samples. Not to mention the samples can be noised or corrupted (CIFAR-10-C).\n- The experiments are not adequate. It is widely acknowledged that OOD detection and generalization are more difficult on large-scale high-resolution datasets. For example, ImageNet benchmarks. The authors do not conduct evaluations on such datasets.\n- In some cases, there may be no clear boundary between covariate-shift data and semantic shift data. As mentioned in recent work[1], these two types of OOD samples can be very hard to separate (even for humans). Could the authors comment on this phenomenon?\n\nGiven the above points, I tend to reject this paper because the overall quality do not meet the expectations of NIPS. However, I may adjust my score if there is a strong argument.\n\n[1] Yang, William, Byron Zhang, and Olga Russakovsky. \"ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms.\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your thorough comments and questions, which we address in detail below.\n\n> *W1. AHA may only work under a rather strict assumption.*\n\nWe 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:\n\n$P_\\text{wild}:= (1-\\pi_s-\\pi_c) P_\\text{in} + \\pi_c P_\\text{out}^\\text{covariate} +\\pi_s P_\\text{out}^\\text{semantic}$\n\nMoreover, 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.\n\n> *W2. I generally believe enhancing OOD detection and generalization with human feedback is laborious.*\n\nOur 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\\%.\n\nRegarding 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.\n\nWe 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.\n\n> *W3. The experiments are not adequate. It is widely acknowledged that OOD detection and generalization are more difficult on large-scale high-resolution datasets.*\n\nThe 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.\n\nResults for both OOD generalization and OOD detection evaluation are summarized below:\n\n| Method | OOD Accuracy | ID Accuracy |FPR95 |AUROC |\n| -------- | -------- | -------- |-------- |-------- |\n| WOODS | $44.46$ | $86.49$ | $10.50$ | $98.22$ |\n| SCONE | $65.34$ | $87.64$ | $27.13$ | $95.66$ |\n| AHA (Ours) | **72.74** | $86.02$ | **2.55** | **99.35** |\n\nThese experiments demonstrate that our method maintains its effectiveness on more complex datasets. \n\n[1] Feed Two Birds With One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. ICML 2023.\n\n> *W4. In some cases, there may be no clear boundary between covariate-shift data and semantic shift data.*\n\nWe 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.\n\nMoreover, 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.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SBlyRnJ2Ir", "reviewer": "Reviewer_CyPp", "summary": "The paper presents a novel approach to the problem of OOD generalization and detection. The authors introduce the AHA (Adaptive Human-Assisted OOD learning) framework, which aims to enhance both out-of-distribution (OOD) generalization and detection by strategically leveraging human-assisted labeling within a maximum disambiguation region. The paper reports significant improvements over state-of-the-art methods with only a few hundred human annotations, demonstrating the efficacy of the proposed framework.", "questions": "- How does the AHA framework perform as the size and complexity of the dataset increase?\n- What are the specific steps taken to mitigate potential biases in human labeling?\n- Can the authors provide more details on the computational efficiency of the AHA algorithm, especially for large-scale applications?\n- How does the framework handle a class imbalance in the context of OOD detection?\n- Are there any specific domains or applications where the AHA framework is expected to be more or less effective, and why?\n- Could the proposed method benefit the OOD detection with unreliable sources [R1] and inspire unsupervised OOD detection [R2]?\n\n----\n[R1] Out-of-distribution detection learning with unreliable out-of-distribution sources. NeurIPS 2023.   \n[R2] Out-of-distribution detection with an adaptive likelihood ratio on informative hierarchical vae. NeurIPS 2022.", "limitations": "NA.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The AHA framework is a creative solution that addresses the challenges of OOD generalization and detection, which are critical for real-world applications of machine learning models.\n- The authors provide extensive experimental results that validate the effectiveness of their approach, showing robust performance across various datasets.\n- The incorporation of human feedback in a strategic manner is a strength, as it capitalizes on the limited labeling budget to maximize model performance.\n- The paper's contributions are articulated, with the novel labeling strategy and the integration of human assistance being the highlights.\n- The transformation of the problem into a noisy binary search is an intelligent methodological choice that allows for the efficient identification of the maximum ambiguity threshold.", "weaknesses": "- While the paper demonstrates strong results, it is not clear how the AHA framework scales with larger and more complex datasets.\n- The reliance on human annotations could be a limitation in scenarios where such resources are not readily available or are cost-prohibitive.\n- The paper could benefit from a discussion on how the findings generalize beyond the tested datasets and scenarios.\n- The computational complexity of the AHA algorithm and its runtime performance on large datasets are not discussed.\n- The paper could address potential biases introduced by human labeling, especially in the context of OOD detection.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for the detailed comments and questions, which we address in detail below.\n\n> *W1. While the paper demonstrates strong results, it is not clear how the AHA framework scales with larger and more complex datasets.*\n\nWe 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.\n\nResults for both OOD generalization and OOD detection are summarized below:\n\n| Method | OOD Accuracy | ID Accuracy | FPR95 | AUROC |\n| -------- | -------- | -------- |-------- |-------- |\n| WOODS | $44.46$ | $86.49$ | $10.50$ | $98.22$ |\n| SCONE | $65.34$ | $87.64$ | $27.13$ | $95.66$ |\n| AHA (Ours) | **72.74** | $86.02$ | **2.55** | **99.35** |\n\n> *W2. The reliance on human annotations could be a limitation in scenarios where such resources are not readily available or are cost-prohibitive.*\n\nThis 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.\n\n> *W3. The paper could benefit from a discussion on how the findings generalize beyond the tested datasets and scenarios.*\n\nWe 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.\n\n> *W4. The computational complexity of the AHA algorithm and its runtime performance on large datasets are not discussed.*\n\nThe 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.\nFor 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.\n\n> *W5. The paper could address potential biases introduced by human labeling, especially in the context of OOD detection.*\n\nHere are the specific steps taken to mitigate potential biases in human labeling: \nWe 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.\n\nMoreover, 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.\n\n> *Q4. How does the framework handle a class imbalance in the context of OOD detection?*\n\nOur 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.\n\n> *Q5. Are there any specific domains or applications where the AHA framework is expected to be more or less effective, and why?*\n\nReferring 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.\n\n> *Q6. Could the proposed method benefit the OOD detection with unreliable sources [R1] and inspire unsupervised OOD detection [R2]?*\n\nThis 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.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces a novel approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild. The proposed approach strategically labels examples within a novel maximum disambiguation region, where the number of semantic and covariate OOD data roughly equalizes. Reviewers agree that this paper is novel to this field and might be an interesting research direction in the field of OOD detection. Key technical questions are addressed well during the rebuttal, and no technical flaws exist.
Decision: Accept (poster)