ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
Authors: William Yang
Byron Zhang
Olga Russakovsky
Venue: ICLR 2024 poster (2024)
Keywords: Out-of-distribution Detection
Failure Detection
Object Discovery
Novelty Detection
Robustness
Primary area: datasets and benchmarks
TL;DR: We introduce a clean semantic shift detection dataset to demonstrate that modern out-of-distributions are overly sensitive to covariate shifts.

Abstract: The task of out-of-distribution (OOD) detection is notoriously ill-defined. Earlier works focused on new-class detection, aiming to identify label-altering data distribution shifts, also known as "semantic shift." However, recent works argue for a focus on failure detection, expanding the OOD evaluation framework to account for label-preserving data distribution shifts, also known as "covariate shift.” Intriguingly, under this new framework, complex OOD detectors that were previously considered state-of-the-art now perform similarly to, or even worse than the simple maximum softmax probability baseline. This raises the question: what are the latest OOD detectors actually detecting? Deciphering the behavior of OOD detection algorithms requires evaluation datasets that decouples semantic shift and covariate shift. To aid our investigations, we present ImageNet-OOD, a clean semantic shift dataset that minimizes the interference of covariate shift. Through comprehensive experiments, we show that OOD detectors are more sensitive to covariate shift than to semantic shift, and the benefits of recent OOD detection algorithms on semantic shift detection is minimal. Our dataset and analyses provide important insights for guiding the design of future OOD detectors.

Published as a conference paper at ICLR 2024
IMAGENET-OOD: DECIPHERING MODERN OUT-OF- DISTRIBUTION DETECTION ALGORITHMM
William Yang", Byron Zhang", Olga Russakovsky
Department of Computer Science, Princeton University, Princeton, NJ, USA (wi 11 amyannnng, z zishuoz, ol ga (cs. princeton.... edu
ABSTRACT
The task of out-of-distribution (OOD) detection is notoriously ill-defined. Ear- lier works focused on new-class detection, aiming to identify label-altering data distribution shifts, also known as "semantic shift." However, recent works argue for focus on failure detection, expanding the OOD evaluation framework to ac- count for aubel-preservin data distribution shifts, also known as "covariate shift." " Intriguingly, under this new framework, complex OOD detectors that were previ- ously considered state-of-the-ar now perform similarly to, or even worse than, the simple maximum softmax probability baseline. This raises the question: what are the latest OOD detectors actually detecting? Deciphering the behavior OOD detection algorithms requires evaluation datasets that decouple semantic shift and covariate shift. To aid our investigations, we present ImageNet-OOD, a clean se- mantic shift dataset that minimizes the interference of covariate shift. Through comprehensive experiments, we show that OOD detectors are more sensitive to covariate shift than semantic shift, and the benefits of recent OOD detection al- gorithms on semantic shift detection is minimal. Our dataset and analyses provide important insights for guiding the design of future OOD detectors]
INTRODUCTION
Out-of-distributin (OOD) detection aims identify test examples sampled from different distri- bution than the training distribution. In the context of computer vision, an OOD detector is simply post-hoc score calibration function that operates a trained image classification model. Previous work has proposed tackling this problem from two perspectives: new-class detection and failure detection. In new-class detection, OOD detectors expected to identify object categories for the purpose of data collection and continual learning (Liu et al. 2020: Hendrycks et al. 2022 Wang al. 2022 Liang et al. 2018 Kim et al. 2022). Recent works have motioned shift the objective from new-class detection to the scenario of failure detection, where OOD detectors are expected to identify misclassified examples to promote the safety and reliability of deep learning models real- world applications (Jaeger et al., 2023] Zhu et al., [022 Averly & Chao 2023 Guérin et al.) 2023). However, evaluating OOD detectors via failure detection benchmarks yields one unanimous finding: no modern OOD detector surpasses the performance of the simple maximum softmax probability (MSP) (Hendrycks & Gimpel, 2017) baseline. In light of this drastic discrepancy, we need to take a step back to address the question: what are modern OOD detectors actually detecting?
Common literature in OOD detection separates distribution shifts into semantic (label-altering) and covariate (label-preserving preserving) shifts (Hsu al. 2020 Tian et al.) 2021 Yang et al. 2021b). Under- standing detection behavior for either type of shift requires proper evaluation datasets that decouple 2021a). Many OOD detection datasets ( (Wang al.]
semantic shifts from covariate shifts (Yang al.
2018 Hendrycks et al. 2021 Galil et al. 2023 set ImageNet- (Russakovsky et al.) 2015) as in- distribution (ID) and subsets of ImageNet-2IK (Deng et al. 2009) as out-of-distribution (OOD). Since ImageNet-IK is also a subset of ImageNet-2IK, both datasets' class labels are derived from the WordNet (Fellbaum) 1998) hierarchy and the images share the same data collection process. However, these datasets often contain contamination from ID images, which violates one of the key
"Equal contribution.
Code and data is at hutp:///gihiacooomiiccccooaaaanr

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Published as a conference paper at ICLR 2024
assumptions of OOD detection for both new-class and failure detection (Bitterwolf et al. 2023
Consequently, several human-annotated datasets have been constructed, though using data sources outside the ImageNet family (Hendryeks et al. 2022, Wang et al., 2022; Bitterwolf et al., 2023 introducing unforeseen covariate shifts due to changes in the data source and collection process.
In this paper, design OOD detection dataset that accurately assess the impact of mantic shift without the influence of covariate shifts. Concretely, we introduce ImageNet-OOD, clean, manually-curated, and diverse dataset containing 31,807 images from 637 classes for as- sessing semantic shift detection using ImageNet- K as the ID dataset. ImageNet-OOD minimizes covariate shifts by curating images directly from ImageNet-21K, while removing ID contamination from ImageNet- IK through human verification. We identify and remove multiple sources of seman- tic ambiguity arising from inaccurate hierarchical relations in ImageNet labels. Additionally, we remove images with visual ambiguities arising from the inconsistent data curation process of Im- ageNet. Using ImageNet- OOD and three ImageNet- K-based covariate shift datasets, we perform extensive experiments on nine OOD detection algorithms across 13 network architectures, from both new-class detection and failure detection perspectives to make the following findings:
1. Modern OOD detection algorithms shifts than semantic shift compared to the baseline MSP (Hendrycks & Gimpel 2017).
even more susceptible towards detecting covariate
2. In ImageNet-OOD, which exhibits only semantic and limited covariate shift, modern OOD detection algorithms yield very little improvement over the baseline new-class detection.
3. Modern OOD detection algorithms only improve on previous benchmarks by ignoring in- correct ID examples rather than detecting OOD examples, causing performance disparity between the task of new-class detection and failure detection.
2 EXISTING DEFINITIONS AND FORMULATIONS
Problem Setup. For image classification, dataset Dtr {(x U:); N. )} sampled from training distribution (z,(x,)) y) is used to train some classifier C X + V. real-world deployments, distribution shift occurs when classifier C receives data from test distribution Pte (x, y) where Ptr (x. y) F (te y) (Moreno-Torres et al. 2012 An OOD detector is scoring function that maps an image x to real number R such that some threshold arrives at detection rule f:
f(z) inndiittibbtionn
if (()) if sxx) <T
out-of-distribbttio
Common changes within the data distribution fall under two categories: covariate shift, which is label-preserving (i.e. concerns examples only from training classes), and semantic shift, which is label-altering (concerns examples only from new classes).
Covariate Shift. Covariate shift occurs in the test data when the marginal distribution with respect to the image differs from the training data: Per (x) * Pte(x)) (Yang et al.) (021b)) while the label dis- tribution remains fixed: (y)lz) Pte (ro (z). ). In this work, we will use three popular datasets with covariate shifts with respect to ImageNet- 1K (Russakovsky et al. 2015): ImageNet-C (Hendrycks & Dietterich 2019) ImageNet-R (Hendrycks et al. 2020)), and Smagehet-Sketch (Wang et al. 2019).
Semantic Shift. Semantic shift occurs when a given set of semantic labels Ytr C Y from the train- ing distribution and semantic labels Yie ) from test distribution have the following property: Ytr n Yze = 0, such that Per (v) = O Vy E Yıe. To best study the effect of semantic shifts, we propose a new dataset, ImageNet-OOD. that minimizes the degree of covariate shifts.
Evaluation. Many OOD detection benchmark commonly use AUROC as threshold free way of estimating detection performance by calculating the area under false positive rate vs. true positive rate curve, considering ID as positive and OOD as negative.
Classical Approach: New-class Detection. The majority of work in OOD detection has formu- lated the problem new class-detection: the notion of IDD is defined by the class labels of the data
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source and therefore is model agnostic. In other words, an image is considered ID if it comes from a class y E Yr and OOD otherwise. OOD detection with this goal focuses exclusively on semantic shift: the detection of novel classes. Although the objective is analogous to that of Open-Set Recog- nition (Vaze et al. 2022), previous OOD detection works often motivated this goal to prevent model failures (Yang al. T.) We provide more intricate discussion on the subtle differences in task formulation of previous OOD detection works in Section of the Appendix.
Modern Approach: Failure Detection. Recent works argued to approach the OOD detection problem from the first principle. Instead of defining "ID" and "OOD" with regard to the data sources, the distinction directly specified by the model's prediction. An image ID if the model correctly classifies an image and OOD otherwise. These recent works converged on the conclusion that recent advances in OOD detection do not result in any improvement for the task of failure detection (Jaeger
et al. 2023 Guérin et al. 2023 Zhu et al., 2022 Averly & Chao
2023).
3 IMAGENET-OODS A CLEAN SEMANTIC OOD DATASET
Recent works in OOD detection have primarily focused on failure detection, but new-class detection is still relevant in practice with the growing interest in adaptive learning systems (Kim et al. 2022 He & Zhu 2022). Consequently, accurate assessment of OOD detection algorithms on semantic shift is very important and needs to be disentangled from covariate shift. Unfortunately. previous datasets were not carefully constructed, leading to contamination from ID classes and unintended covariate shifts. We highlight the shortcomings of past OOD datasets and introduce ImageNet-OOD, a carefully curated, semantic OOD dataset designed to overcome these challenges.
3.1 PITFALLS OF EXISTING SEMANTIC SHIFT DATASETS
Early evaluation frameworks in OOD detection primarily use small datasets such CIFAR-IO, CIFAR- (Krizhevsky) 2009), SVHN (Netzer et al. 201T), and MNIST (Deng 2012), but their low-resolution and limited number of classes fail to extend real-world conditions. Consequently, the outcomes of OOD detection methods in these restricted environments can substantially deviate from those in expansive settings. To include more diverse scenarios, recent OOD detection datasets often designate ImageNet-IK IK as the ID dataset (Hendrycks et al., 2022; Huang & Li] 2021, Galil (et al. ).23). Nevertheless, these contemporary datasets frequently present issues, such as semantic or visual ambiguities and introduction of unnecessary covariate shifts.
Semantic Ambiguity. Several existing datasets overlook the hierarchical relations in ImageNet la- bels, leading to ambiguity in deciding whether semantic concept is OOD, For example, ImageNet- O (Hendrycks et al.] 2021) contains images from the class "pastry dough," which is contained in the "dough" class in ImageNet-1K. We also observe this contamination in OOD datasets that do not utilize ImageNet-211 K classes, such as Species (Hendrycks et al.) (2022). For example, the ImageNet- 1K dataset contains the class "Agaric", which includes the Species class "Agaric Xanthodermus."
Visual Ambiguity. Although several datasets remove ID contamination through human filtering, they overlook visual ambiguity attributed to the intricacies in the data collection process of Ima- geNet. OOD classes can show up in ID images even though the ID and OOD classes are far away on the WordNet semantic tree (Fellbaum 1998). For example, the C-OOD benchmark (Galil al.) 2203) contains the class "basin," while ImageNet- KK contains the class "lakeside." While "basin" is technically OOD, many images from the "lakeside" and "basin" classes are visually indistinguish- able, and thus, can be labeled interchangeably.
Unnecessary Covariate Shifts. Several common OOD datasets used when ImageNet- is des- ignated as ID include iNaturalist (Horn et al., 2018). SUN (Xiao et al. (010)), and Texture (Cimpoi) ctt al. 2014). However, these datasets come from very specific domains and thus lack the semantic diversity that is reflected in real-world scenarios. Recent works such NINCO (Bitterwolf et al.) 2023) and OpenImage-O (Wang et al. (022) encourage semantic diversity through manual selection of OOD images. However, due to the limited number of images, limited number of OOD classes, and/or deviation from the original ImageNet data collection procedure, the resulting dataset can potentially introduce hidden covariate shifts that OOD detection algorithms exploit.
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ImageNet-IK Ice Cream
ImageNet-2IK
ImageNet-IK Violin
ImageNet-2IK
Frozen Dessert
Viola
Classes
Animal
Vehicle
ImageNet-IK
ImageNet-21K
ImageNet-1K
ImageNet-21K
Cattle
Herbivore
Scuba
Aqualung
OOD
Figure I: Removing ambiguities in ImageNet-OOD. We identify classes in ImageNet-21K which should nor be included in the ImageNet-OOD dataset, since it would be ambiguous whether they are truly OOD with respect the ImageNet- 1K classes. Left: Semantic Ambiguity. "Frozen Dessert" in ImageNet-21K (Hendrycks et al. 2021) should not be considered OOD as it is hyponym of "Ice Cream." Additionally, classes associated with organism problematic in the WordNet hierarchy: "Herbivore" contains images from the ImageNet-IK class "Cattle" but it is neither a hypernym a hyponym. Middle:
seatintiiaallggrounnee Covariate Shifts. A dog vs. vehicle classifier can also be thought of animal vehicle classifier. Given this classifier, it is unclear whether "cat" should be considered OOD. Right: Visual Ambiguity. "Violin" and "Viola" or "Scuba Diver" and "Aqualung" are visually indistinguishable human labelers, leading potential annotation error.
3.2 CONSTRUCTION OF IMAGENET-OOD
We start with the 1000 ImageNet-IK classes as ID. These classes are directly sampled from ImageNet-2IK, which contains images illustrating the 21K nodes in the WordNet semantic tree (Fellbaum) 1998). We begin the construction of ImageNet-OOD with pool of candidate classes from the processed version of ImageNet-211 (Ridnik et al. 202T). To reduce semantic ambiguity, iteratively remove classes based the following criteria:
All ImageNet- 1K Classes, Their Hypernyms, and Hyponyms. Classes in ImageNet-IK: are sim- ply a subset of classes in ImageNet-22 according to the WordNet semantic tree. Since ImageNet- 21K and ImageNet- 1K followed the same data collection procedure, the degree of unwanted covari- ate shift is minimized (Galil et al. I 2023). Consequently, these datasets often propose selecting OOD images from the set of ImageNet-22K classes that are disjoint from ImageNet-16 K classes (Wang) et al. 2018: Hendrycks et al. 202I). However, the hierarchical structure of WordNet allows hy- pernyms (semantic ancestors) and hyponyms (semantic descendants) of ImageNet- classes to contain ID images (e.g. "Ice Cream" vs. "Frozen Dessert" in Fig. Left). We remove hypernyms and hyponyms ID classes to promote accurate reflection of OOD detection performance.
Hyponyms of "Organism". Natural beings in WordNet and ImageNet-21K are categorized by both technical biological levels and non-technical categories, which leads to inconsistencies in hi- erarchical relations. For instance, although "Herbivore" is intuitively a hypernym of the ID class "Cattle," such relationship is not captured by the WordNet semantic tree. Therefore, we avoid this type of ambiguity by removing all the hyponyms "Organism."
Covariate Shifts. The definition of "semantic" vs. "covariate" shift be- comes ambiguous if the learned decision boundary lies in higher levels of the semantic hierarchy. Consider the scenario in Fig. | Middle, where "dog" and "vehicle" are the only ID classes. The leamed classifier also be considered "animal" vs. "vehicle" classifier. Then, "cat." although technically a semantic shift, can also considered a semantically grounded covariate shift with the label "animal." This scenario is very similar to subpopulation shift where bias in the data collection
Semantically-grouut

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Percentiles
10th
30th
50th
70th
90th
PASS Distances
Sowee TAN
MSP
Shane
Duen
Energy
Fiar 2.2
VIM
ReAct
Figure 2: Examples of Images from ImageNet-OOD. Images around the 10th, 30th, 50th, 70th, 90th percentile based on either the distance to the closest ImageNet- 1K image using features from self-supervised ResNet-50 pre-trained on the PASS dataset (Asano et al., 2021 or scores from OOD detectors MSP (Hendrycks & Gimpell 2017). Energy (Liu et | 2020). VIM (Wang et al. 2022). and ReAct (Sun et al. 2021). Within each pair, the left image the MengeNet-OOD image and the right image is its closest image in ImageNet-IK: IK. These examples illustrate the diversity of ImageNet- OOD and its visual similarity ImageNet- 1K despite having different semantics and OOD scores.
process results in overdomaince of a subclass. Using the WordNet semantic tree, we determine the most general decision boundary for ImageNet-1K. We identify the common ancestor for every pair of ImageNet- IK classes and position each ImageNet- KK class one level below any one of the com- mon ancestors. Subsequently, we redefine all the ImageNet- IK classes to the class defined by this decision boundary during our dataset construction process.
Final Class Selection. Despite removing ambiguous classes, manual effort is still needed solve visual ambiguities between ImageNet-IK and the rest of ImageNet-2IK images. The authors of ImageNet noted that flaws in the construction process of ImageNet-21K can introduce labeling errors due to visual ambiguity across classes (Russakovsky et al., 2015). For instance, the classes "Viola" and "Violin" are virtually indistinguishable unless compared side by side to discern their sizes, thus bringing some degree of human labeling errors. Consequently, the OOD dataset might be contaminated with images from ID classes. Additionally, certain OOD labels are distant on the WordNet semantic tree but contain images with similar semantics. For instance, nearly all images from "Aqualung" (ImageNet-21K3 K) and "Scuba Diver" (ImageNet-Il IK)) depict a scuba diver wearing an Aqualung. since Aqualung is apparatus essential for scuba divers breathe underwater. To avoid the visual ambiguities illustrated in Fig. [ Right, we spent 20 hours to manually select 637 classes from the remaining set that are distinguishable from ImageNet- 1 classes by iteratively examining the WordNet neighbors of each ImageNet- KK class and randomly selecting 50 images. Following this, a final 6-hour review conducted filter out any images that might have been mislabeled. This process is described in greater details in Section Hf the Appendix.
3.3 IMAGENET-OOD STATISTICS
ImageNet-OOD contains a total of 31,807 images from 637 classes. Our construction methodology naturally results in a dataset that is diverse as ImageNet-IK and also very similar visually to ImageNet-1K IK images. To illustrate this, Fig. 2 displays an amalgamation of images that very diverse in terms of both semanties and visuals, ranging from fried eggs capacitors, and objects against plain backgrounds to complex scenes. Additionally, the nearest ImageNet- image for each ImageNet-OOD image appears to be in very similar domain.
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MSP
Energy
Original Image
Zoomed Image
0.82
2.68
0.81
AUROC
0.80
0.62
0.79
Ostrich: 1.00
Ostrich: 0.98
Inagchot
ImugeNot.
04
0.6
0.8 Distance
1.0
0.4
06
o'a
1.0
ImageNet- MOD Percentlie
Distance
100.0
200.5
ViM
ReAct
65.
Percentile
F1..2
0.500
NU80C
0.875
MUROC
0.050
70.0
0.825
2.70
60.0
63.4
Imagahatt Imagahes-R
ImageNel- D00
2.65
imageNet.tt
50.0
0.0
1.0
0.4
1.0
Distance
Distance
Figure 3: Influence of Covariate Shift on OOD Detection. Left. Relationship between OOD de- tection performance and the average distance to the closest ImageNet- KK (Russakovsky image using features from self- supervised models trained on the PASS (Asano et al. [022 dataset. Results reveal that given similar PASS feature distances between subsets of the two datasets, mod- ern OOD detection algorithms elicit stronger response covariate shift (ImageNet-R ( (Hendrycks) jet al. 2020)) than semantic shift (ImageNer-OOOl OOD). Right. An image of Ostrich ImageNet- dataset where elementary zoom transformation is applied. The transformation did not influence the model prediction, but substantially decreased the ranking ViM (Wang et al. 2022) and Re- Act (Sun et al. 2021) scores in ImageNet-OOD by 38.4%, 39.6%, respectively.
2015)
EMPIRICAL ANALYSIS
Equipped with ImageNet-OOD, we now analyze the performance of OOD detection algorithms un- der semantic shift with limited covariate shift. First, demonstrate that modern OOD detection algorithms are more susceptible to covariate shifts. We reveal that even for images with similar dis- tance ImageNet- IK, modern OOD detection algorithms perform better detecting images from covariate shift dataset ImageNet-R (Hendrycks et al. (2200) than images from ImageNet-OOO.. Ad- ditionally, we design simple sanity check for OOD detection on random untrained models. Our result show that OOD detection algorithms fails the sanity check and elicit strong response to covariate shift. Finally, through an extensive evaluation of nine OOD detection algorithms and six datasets over 13 network architectures, we demonstrate that many modern OOD detection algo- rithms do draw practical benefits both new-class detection and failure detection scenarios.
Our experiments include nine logit-based and (eature-based OOD detection algorithms, which are more practically adopted due to their require minimal computational cost (Yang et 202Tb). Logit- based methods MSP (Hendrycks & Gimpell 2017). Energy (Liu et al. 2020). Max-Cosine (Zhang) & Xiang 2023 3. and Max-Logit (Hendrycks et al., (2022) derive scoring functions from classifica- tion logits because OOD examples tend to have lower activations. Feature-based methods Maha- lanobis (Lee et al. 2018), KNN (Sun et al. (022), ViM (Wang et al. 2022), ASH-B (Djurisic et al.) 2022). and ReAct (Sun et al. (2021) operate the penultimate layer of the model. Hyperparameters
are selected based on ablation studies on ImageNet- 1K done by original authors.
4.1 COVARIATE SHIFTS CONFOUND DETECTION OF SEMANTIC SHIFT
begin by demonstrating that modern OOD detectors are highly sensitive to covariate shift. Con- cretely, they cannot only detect large proportion of semantic shift data without simultaneously detecting a large proportion covariate shift data. To illustrate this, we partition subsets im- ages with varying degrees of visual similarity to ImageNet-1K, the ID data, with half the subsets exhibiting covariate shift and half exhibiting semantic shift. Specifically, we partition ImageNet-R (Hendrycks al. 2020) and IOD datasets into 100 subsets each. In each set, images

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have similar distances to the closest ImageNet- 1K (Russakovsky et al. (015) image using features from a MoCo-v2 (Chen et al.] 2020) self-supervised ResNet-50 (He et al. 2016) model pre-trained on the PASS dataset (Asano et al. 202I), which is ImageNet replacement derived from YFCC- 100M (Thomee et al. 2016). Next, we feed each image into a ResNet-50 classifier trained on ImageNet- 1K to obtain the OOD scores. Finally, we calculate the AUROC between images of each subset and images from ImageNet-IK, to analyze the relationship between OOD detection perfor- mance (AUROC) and the average PASS distance to the closest ImageNet- IK image of each subset.
We make a number of observations shown in Fig. 3 Left. First, as one would expect, there is a positive trend between the OOD detection performance and average PASS distance, as images "farther" from the training distribution have been shown to be easier to detect (Fort et al.) 20021) The trend seems to be stronger in modern OOD detection algorithms such as Energy (Liu et al., 2020), ViM (Wang et al. 2022), and ReAct (Sun et al., (021) than in the baseline MSP (Hendrycks) & Gimpel 2017). More importantly, modern detection algorithms are clearly better at detecting ImageNet-R images than ImageNet-OOD images, even with similar PASS distances. To verify this quantitatively, we fit a linear regression model between PASS distance and AUROC for each of the datasets to account for the effect of PASS distance AUROC. Our results reveal that for ViM, Energy, and ReAct, the 95% confidence intervals on the intercept of the regression trained between the two datasets do not overlap. As a result, there is statistical significance suggesting that the effect of ImageNet-R naturally induces a higher detection performance than ImageNet-OON on ViM, Energy, and ReAct. The full results and details are included in the Section Cf the appendix.
To further demonstrate that OOD scores are affected by covariate shifts, we illustrate that upon introducing a modest covariate shift, there is notable decline in the scores of OOD detectors. Fig. [3] Right displays an image from ImageNet-IK, depicting an ostrich. A ResNet-50 classifier predicts the image correctly with a confidence score around 100%, which ranks higher than 99% of confidence on ImageNet-OOl OOD images. Three modern OOD detectors also assign a score to this image that ranks it higher than 99% of the images from ImageNet-OOD. After applying a zoom to the ostrich, the classifier still exhibits a high classification confidence score of 98%, which still ranks higher than 95.3% of ImageNet-OOD. However, the OOD detection scores rankings from Energy, ViM, and React drops to only 83.7%, 61.6%, and 61.4% higher than the ImageNet-OOD samples, respectively, despite there being no change in semantic label or model prediction.
4.2 OOD DETECTION ALGORITHMS FAIL SANITY CHECK ON COVARIATE SHIFT DATASETS
After observing how sensitive modern OOD de- tection algorithms are to covariate shifts, we de- sign sanity check to further test this sensitiv- ity in a much stronger setting. Previous works demonstrated the power of random models as feature extractors for tasks such as in-painting, super resolution, and interpretability (Adebayo
OOD Detection on Random Models
Random
IN-C (Noise)
IN-00D
ctt al. 2018 Saxe et al. [2011] Alliin & Bengio 2016 Ulyanvv et al. (018)) While having this
AUROC
0.4 83
power is acceptable for other tasks, it is very problematic in the context of OOD detection as it challenges the fundamental concept of ID and OOD. The idea of ID becomes ill-defined on random models as the model has not encoun- tered or learned from data sampled from any distributions. Therefore, for a randomly initial- ized model, the concept of ID should not exist.
MSP
Mox Logits
Energy
Mahatanobis
ViMi
KwN
Prtlct
Given that every data point should be con- sidered OOD for a random model, a well- behaved OOD detection algorithm should per- form around random chance (AUROC = 0.5) (Hendrycks & Gimpel 2017). We design sim- plc sanity check around this idea and found that OOD detection algorithm is biased toward certain covariate shifts. Performance of seven commonly
Figure 4: Performance of OOD detection un- der random models. Five ResNet-50 models (in- dicated by color) with random parameters were evaluated on ImageNet-R (IN-R), ImageNet-C (IN-C) and ImageNet-OOD (IN-OOD).
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Table 1: Performance of OOD Detection Algorithms. We evaluate seven modern OOD detection algorithms across 13 models, three covariate shift datasets: ImageNet-C (IN-C), ImageNet-R (IN- R), ImageNet-Skette
(K--ckecch) and two semantic shift datasets: ImageNet-OOll and Openlmage- O, under the goals of both new-class detection and failure detection. Results in each column denotes AUROC of each pair
vs. OOD datasets. The low AUROCs of the In-C. IN-R, IN- Sketch vs. IN-OOD experiments indicate that OOD detection algorithms lose their ability to perform new-class detection under the presence covariate shifts that these datasets introduce. Moreover, the improvement of the best-periie pest-performing detector is only 0.7% in the IN-IK vs. IN-OOD experiment. The results also confirm that MSP still outperforms all modern methods under failure detection.
IN-C
IN-R IN-OOD
IN-Sketch IN-OOD
IN-1K
IN-1K
Goal
Method
IN-OOD
OpenImage-O
IN-OOD
MSP
55.5
48.0
46.4
84.6
79.8
Max-Logit
52.3
40.4
41.0
87.9
80.5
Energy
51.5
38.9
39.9
87.6
79.9
Mahalanobis
43.6
37.8
30.7
76.0
64.8
New-class Detection
ViM
46.4
35.9
31.8
88.9
79.8
KNN
39.7
36.9
26.3
76.0
59.3
Max-Cosine
55.4
36.3
38.0
85.8
80.7
ASH-B
51.5
46.2
41.8
90.1
79.5
ReAct
41.4
35.0
36.9
82.7
63.4
MSP
80.8
76.1
76.8
90.0
86.9
Max-Logit
76.9
68.9
72.6
89.2
84.1
Energy
75.5
66.4
70.9
88.5
83.1
Mahalanobis
57.1
58.4
51.0
74.5
65.9
Failure Detection
ViM
70.7
67.1
64.7
88.7
82.5
KNN
51.9
52.7
47.2
68.7
55.0
Max-Cosine
73.5
71.5
72.6
88.5
84.8
ASH-B
67.7
71.7
70.4
88.3
81.0
ReAct
60.2
47.9
55.9
80.4
65.7
used OOD detection methods is evaluated using five random models on the ImageNet-R (Hendrycks)
et al.
2020 images and on blurring and noise corruptions in ImageNet-CC (Hendrycks & Dietterich,
2019
This experiment is performed on Resnet-50 with Kaiming normal initialization (He al.
2015
We use only the most severe corruptions in ImageNet-C.
Fig.
reveals that OOD detectors confidently detect blurry ImageNet-C images as OOD (AUROC
0.5) and noisy ImageNet-C images as ID (AUROC < 0.5). Additionally, they also tend to detect ImageNet-R images as ID. ImageNet-OOD, on the other hand, unaffected and detected around random chance (AUROC 0.5). The bias toward detecting certain corrupted images illustrates that OOD detectors can easily pick up patterns from covariate shift, even on untrained models.
4.3 MODERN OOD DETECTION ALGORITHMS DO NOT BRING PRACTICAL BENEFITS
In this section, we show that modern detection algorithms do not gain significant benefits over the MSP (Hendrycks & Gimpel 2017 baseline regardless of the approach OOD detection. Under new-class detection, we reveal that when covariate shifts are minimized, modern detection algo- rithms have less than 1% AUROC improvement over the MSP baseline. Furthermore, comprehen- sive analysis under failure detection reveals that modern OOD detection algorithms do not actually improve at distinguishing between correctly classified examples and semantically OOD examples.
For the evaluation, we used 13 different convolutional neural networks trained on ImageNet- 1K from the torchvision library: ResNet(He et al. 2016), DenseNet (Huang et al. (017). Wide ResNet Zagoruyko & Komodakis 2016), RegNet (Xu et al.) 2022), ResNeXt (Xie al. 2017). The algo- rithms were evaluated across five different datasets: ImageNet-C (Hendrycks & Dietterich] 2019). ImageNet-R (Hendrycks al. 2020), ImageNet-Skenn Skeecch Wang et al. 2011)). Openlmage-O (Wang et al. 2022), and ImageNet-OOD The results from Table I reports the average AUROC across the 13 models under both new-class and failure detection scenarios.
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New-class Detection. New-class detection aims detect only examples from semantic shift datasets. All images that belong to training classes, including covariate shifted examples, are con- sidered ID. We first demonstrate that under the influence of certain covariate shifts, OOD detec- tion algorithms are not able to identify semantic shifts. Table | displays AUROC scores on three covariate shift datasets on ImageNet-Sketch and ImageNet-R vs. ImageNet-OODD indicating a less than 50% probability that OOD detector scores an ImageNet-OOD example higher than an ImageNet-Sketch example. However, MSP yields significantly higher AUROC than other OOD detectors, revealing that modern OOD detectors are more susceptible towards covariate shift.
ImageNet-OOD. All detection algorithms show AUROC below 50%
Next, we demonstrate that improvements gained on past datasets disappear when modern OOD detection algorithms are evaluated on ImageNet-OOD. The discrepancy in AUROC between ImageNet-1K IK vs. Openlmage-O) and ImageNet-1K: KK vs. ImageNet-OOD highlights the importance of the semantic shift dataset. In partieular, on OpenImage-O. Max-Logit ViM, and ASH-B all exhibit significant improvements over the MSP baseline, showing 3.3%, 4.3%, and 5.5% enhancement in AUROC, respectively. However, when evaluated on ImageNet-OOD, the improvements drasti- cally shrank to 0.7% for Max-Logit, completely disappears for ViM and even decreased by 0.3% for ASH-B. B. We provide qualitative explanations for this phenomenon With the insignificant improvement under semantic shift coupled with the increased susceptibility towards covariate shift, it is unclear whether modern OOD detection algorithms yields any practical improvements over the baseline when deployed to the real-world.
Section[Gjin the Appendix.
Findings on Failure Detection. Failure de- tection aims detect incorrectly classified ex- amples regardless of the type of distribution shift with semantic shift examples being incor- rect by definition. Results from Table T con- firms previous findings that MSP outperforms modern OOD detection algorithms under fail- ure detection across all experiments. Interest- ingly, on the ImageNet- 1K ImageNet-OOD experiment, MSP outperforms Max-Logit by 2.8% under failure detection despite Max-Logit outperforming MSP by 0.7% under new-class detection. This discrepancy is particularly an unexpected outcome since semantic shift exam- ples are considered OOD for both failure and new-class detection. Breaking down the test data by whether or not they are correctly pre- dicted resolves this mystery. Fundamentally, the goal of an OOD detection algorithm to give lower scores, and hence, lower rankings, semantic shift data (Equation TT. However, ob- serve from Fig. 5 that Max-Logit does not rank the semantic shift (ImageNet-OOD) examples lower (more OOD) than MSP does. Instead, it ranks the incorrect test (ImageNet-IK) examples higher. In other words, Max-Logit is neither better at detecting semantic shift examples (Fig. 5 Right) nor better preserving correct test examples (Fig. Left). Instead, is better at preserving incorrect test examples (Fig. 5) Center), which is the opposite of the ideal behavior for catching model failures.
Rank Differances
Max-Logits
Imagettet (Comect) Scones Imagettet uncorrect Scores
Imagelee-00n Scores
Mac
40
1010
43
116
Figure 5: Comparison of the ranking between MSP and Max-Logit. Left. MSP is slightly bet- ter at ranking correctly predicted ImageNet im- ages higher. Center: Max-Logit ranks more in- correct ImageNet images higher than MSP. Right. MSP and Max-Logits have near identical ranks on ImageNet-OOD examples.
5 CONCLUSION
We introduce ImageNet-OOD, a carefully curated, diverse OOD detection dataset for studying the effects of semantic shifts. By building on top of ImageNet-21K and manually selecting test classes to remove semantic and visual ambiguities, we avoid unnecessary covariate shifts that may confound the performance of OOD detection algorithms. Using ImageNet-OOD, we reveal that many modern OOD detection algorithms detect covariate shifts to a greater extent than semantic shifts and further demonstrate that the improvement of many algorithms disappears. We hope that dataset and findings will help the OOD detection community in building methods that are more effective at detecting semantic shifts and aligning their behavior with their stated purpose.

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ETHICS STATEMENT
Although our dataset and analysis does not directly produce harmful impact, we note that the bias from ImageNet may be propagated as ImageNet-OOD reuses images from ImageNet-21K (Deng) et al., 2009; Yang et al. 2020). As a means of mitigation, we manually filter out inappropriate classes In addition, OOD detection becomes a high-stake task prevent catastrophic failures in computer vision systems. While we strive to include diverse set of classes in ImageNet- we cannot guarantee that performance on ImageNet-OOf is the valid indicator for all safety-critical applications.
REPRODUCIBILITY STATEMENT
We provide the class names and synset IDs for all ImageNet-OOD classes in the appendix. In addition, we provide all image filenames as presented in the ILSVRC 2012 Challenge (Russakovsky et al. 2015), as well as sample code for the experiments in the linked Github repository linked in the abstract.
ACKNOWLEDGEMENTS
This material is based upon work supported by the National Science Foundation under Grant No. 21 12562. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors' and do not necessarily reflect the views of the National Science Foundation. We also thank the Princeton VisualAI Lab members and Christiane Fellbaum for helpful feedback.
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Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Rongxin Jiang, Bolun Zheng, and Yaowu Chen. Rethinking out-of- detection from a human-centric perspec- tive, 2022.
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APPENDIX
In this appendix, we will provide more detailed analysis on claims made in the main paper.
Section A We provide an related work on covariate shifts in OOD detection, failure detec- tion, and different OOD detection datasets used in the paper.
Section B]
We extend Section I. the main paper analyze the difference between score distributions across multiple OOD detection algorithms.
Section C
We supplement Section 4.1 of the main paper on quantitative results using
PASS Asano al., 2021) distances.
Section D
We analyze the behavior of methods developed for Open-Set Recognition and gradient-based OOD detection.
Section D
We supplement Section 4.3 of the main paper and provide more analysis on the
separability between correct and incorrect ID examples.
Section We supplement Section 4.2 of the main paper and perform the sanity check to examine covariate shift bias from different model architectures.
F
Section G We supplement Section of the main paper to provide qualitative examples of images revealing the inherent bias modern detectors towards certain covariate shift.
Section H We supplement Section the main paper and provide details on the construc- tion process of the ImageNet-OOD dataset.
Section I We supplement Section 3 of the main paper and provide class names and synset IDs for the 637 ImageNet-OOD classes.
A RELATED WORK
Covariate shift in OOD defection. Covariate shift was first considered in OOD detection by Gen- eralized ODIN (Hsu et al. 2020), where OOD detection performance was evaluated considering all covariate shifted examples as dittof-disttibbutin. (Tian et al.] (02T) designed scoring function that disentangles detection of semantic vs. covariate shifts. Later, (Yang et al. 2023) pointed out that models should ideally generalize, instead of detect, in the case of covariate shifts, because general- ization is the primary goal of machine learning. Thus, they defined all covariate shifted examples as in-distribution and proposed several benchmarks that include covariate shifted data. Recently proposed benchmark OpenOODv1.5 (Zhang et al., 2023) coined the term full-spectrum detection to encapsulate this idea. They found that under this setting, OOD detection performance are sig- nificantly hindered in contrast to the traditional setting. While new-class detection characterizes all covariate shifted examples as ID, failure detection only characterize the correctly classified ones as ID, since the rejection of covariate shift examples that would otherwise hurt the model's classifica- tion performance should not be penalized.
Failure Detection. OOD detection often categorized as sub-task failure detection, as the primary goal of OOD detection is to catch unsafe prediction before models make mistake. Other tasks that share this common goal of failure detection include misclassification detection (Hendrycks) & Gimpel| 2017) and uncertainty calibration (Kendall & Gall 2017). both of which differs from OOD detection in that they do not consider examples outside the set of training classes. However, since all failure detection methods can be interpreted as scoring functions and motivated from a common principle (alerting failure before occurrence), (Jaeger et al., (023) argues that all failure detection tasks should be evaluated across common benchmark that takes into account the classifi- cation performance of the model. (Xia & Bouganis
2022) made similar arguments under Selective Classification in the presence of OOD Data (SCOD).
Datasets for OOD detection. In the experiments of the paper, we used three covariate shift datasets: ImageNet-R (Hendrycks et al. 2020), ImageNet-C (Hendrycks Dietterich, 2019), and ImageNet-Sketch (Wang et al. 2019). ImageNet-R has 30,000 images containing renditions of 200 ImageNet-1K classes. These renditions include art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos,
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ImageNet
Imagetiet Imagetle:-C 000g
imgelee:
magelett
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mageleuOC ImsgeNen-000
magelee-CC
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imgelket
Imegetiet 000
Dunutty
ounatty
Dendty
Inagefett
Imagshet imagheet
Imagehet Imaeeket
Imagetett
Imageten:000
imagelen DOD
imageket 010
berkks
benskt
Figure 6: Distribution of OOD scores on ImageNet-C. A kernel density estimator further il- lustrates that all OOI) detectors detect covariate shift. Results reveal that for both covariate shift datasets ImageNet-C, the distribution of scores is lower than that of semantic shift.
Distribution of COD Scores for ImageNeet-R
Imageliet ImaguNazt Imaguiezt 000
Imageket
imaeeket
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mapphez
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impphee COD
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Imapelee--OD
Imapekee--ck
Danaly
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Demily
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React Scores
Figure 7: Distribution of OOD scores on ImageNet-R. Same setup as Figure but on ImageNet-R and reaches the same conclusion.
toys, and video games. ImageNet-C includes corrupted versions of ImageNet-IK images at dif- ferent severity levels using corruptions such brightness, contrast, elastic, pixelate, and JPEG. ImageNet-Sketch contains 50,000 images of sketches of all ImageNet- classes. We also used OpenImage-O (Wang et al., 2022), manually curated semantic shift dataset, which is derived from OpenImages (Kuznetsova al. 2020), an object detection dataset.
B ANALYSIS OF SCORE DISTRIBUTIONS
In the following experiment, we will compare the score distributions of OOD detectors on semantic and covariate shift datasets to further show that that OOD detectors cannot only reject large propor- tion of semantic shift data without simultancously rejecting a large proportion correctly classified covariate shift data.
We provide complete analysis across seven OOD detection algorithms on their distribution of scores between covariate and semantic shift. Fig. 6 and that all seven algorithms rank significant proportion of covariate shift examples lower than semantic shift examples. Additionally, breaking down covariate shift examples between correct incorrect, Fig. and 9 reveals that all seven
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Distribution of COD Scores for ImageNettCC
ImageNes-C (corect) ImageNees (inomrct))
Imaphhes (coneet) imapeeez (ncomect) imggekei-002
maaphet C (sonect) imaphhet (Incomec)
Imapehiet C (cernet) Imagehict (incorsct) Imw-ehset-OoD
Imapellei-O0c
Imapelet-30D
Daralty
Danalty
Chernity
MSP Scores
Mox Lagit Scores
Energy Scores
Mahalanbeli Scores
Imageliet-C (comeet)
Imagetles-C (nnomertt
imsgekin: Incomeet)
Imagekit (hhomeet)
beraity
Deratty
Ceaaity
VM Scores
KOVN Scowe
ReAct Scores
Figure Score breakdown on covariate shift data on ImageNet-C. Comparison of detection score distributions for correctly classified covariate shift examples on ImageNet-C and semantic shift examples (ImageNet-OOD) Scores correct ImageNet-C examples tend to even lower than ImageNet-OOD examples. Rejecting significant portion of semantic shift data leads to the rejection of a significant portion of correct covariate shift data.
Distribution of ROD Scores for ImageNet-R
Imageltec-tt (tomect) Imageliet (incoment)
Imapekes-Rt (conect) imageket (ncomect) Imageken COD
Imapeleee et (conect) imageket (Inornect)
Imagahiet-Rt (cornec)) Imegetiit R (Incortec)
Imagelee--Odn 000
magekket acD
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Denskz
Dondit
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Lagit
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imapehet (coeet)
imaehett (corret) ImapehettA (Inomnec)
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imapthes- (ncomezt)
ImaguNes 00p
Imapehes COD
Imiapehit 0C0
Dana
Dana
VI Scores
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ReNct Scores
Figure 9: Score breakdown covariate shift data ImageNet-R. Same setup Figure but on ImageNet-R. Scores of correct ImageNet-C examples tend to be similar to ImageNet-OOD examples.
Table Proportion of correct examples discarded by rejecting 75 percent ImageNet-OOD examples. Using the threshold where 75 percent of ImageNet-OOD examples are rejected as OOD, how many correct examples from ImageNet-1K, ImageNet-C, and ImageNet-R are discarded, which hurt model safety. Results reveals that modern OOD detection methods does not show significant lower false rejection rate on ImageNet-IK and have significantly worse false rejection rate when covariate shift is introduced by ImageNet-C and ImageNet-R.
ImagoNer
Hendrscks Gimpel 2017
Max-Cagir PRaiarress
kneegg Lipge A010
19.3
67.4
61.2
Mobalanghit Teae 2018
500
91.7
80.2
22.2 ViM Wirke Lain 2014
85.3
58.8
KNN
57.8
92.3
83.1
ReAe
27.1
73,5
68.0
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Published as a conference paper at ICLR 2024
MSP
Energy
Method
Dataset
Value
0.62
0.88
0.001
0.011
0.61
IN-R
0.804
0.007
AUROC
AUROC
MSP
0.008
0.011
0.79
0.82
IN-OOD
0.791
0.007
0.78
Imagcfect 000
Imagefict OOD Imagefict
IN-R
0.012
0.009
0.77
Imagefeet
Energy
0.862
0.006
0.010
IN-OOD
0.767
0.006
ViM
ReAct
0.55
0.40
0.114
0.007
IN-R
0.812
0.005
0.50
VIM
0.163
0.121
AUROC
0.85
AUROC
0.75
IN-OOD
0.701
0.007
0.70
IN-R
0.154
0010
0.738
0.007
0.65
ReAct
0.75
0.515
0.010
IN-OOD
0.4
0.5
1.0
0.4
0.6
0.6
1.0
0.402
0.016
Distance
Distance
Figure 10: Full Analysis on influence of Covariate Shift on OOD Detection. Left. Relationship between OOD detection performance and the average distance to the closest ImageNet- 1K (Rus-) 2015) image using features from self-supervised pre-trained models on the PASS 2021) dataset. This figure adds linear regression model with its 95% confidence interval to Fig. 3 The results augments findings in section 4.] by revealing substantial overlap in confident region MSP but low overlap for Energy, ViM, and ReAct. Right. Quantitative measures for each of the fitted linear model with given form AUROC = (distancce) + a. The 95% confi- dence interval for the a between the two datasets do not overlap for Energy. ViM, and ReAct, but do overlap for MSP. This means that the difference in intercept coefficient is statistically significant for Energy, ViM, and ReAct but not MSP.
sakovsky et al.
(Asano et al.
algorithms do indeed confuse correctly classified covariate shift examples with semantic shift amples. Finally, Table 2 uncovers that all seven algorithms yield significantly more rejection of correctly classified examples with covariate shift datasets. This provide comprehensive support for the conclusion drawn in Section 3.2 of the main paper.
C QUANTITATIVE ANALYSIS ON PASS DISTANCES
In this section, we provide full details on the quantitative measures of the relationship between PASS (Asano et al.
202T) distance (image similarity) and AUROC (OOD detection performance). The results in Fig. 10 Right confirms that there is a statistical significant difference on the per- formance of OOD detection between covariate shift (ImageNet-R) and semantic shift (ImageNet- OOD). Using the standard error, we can derive the 95% confidence interval for the intercept measure of the linear regression model. For example, using two times the standard error, we can derive for energy the respective confidence interval between ImageNet-R and ImageNet-OOD as (0.85, 0.874) and (0.755, 779). Since these two intervals do not overlap, there is statistical significance that real intercept between the two datasets is different. Intuitively, the intercept of the model measure the effect of dataset independent from PASS distance on OOD detection performance. Since ap- pears the intercept for the ImageNet-R model is higher, this suggests covariate shifts tend to boost AUROC, demonstrating that OOD detectors detect covariate shift more easily. Similar conclusions can be reached for ViM and ReAct, but not MSP, revealing modern algorithms are more susceptible to covariate shifts. Fig. 10 Left unravel the performance different between the two datasets across different PASS measures. We observe that the confident region of the linear regression model have substantial overlaps for MSP and minor overlap for ReAct. On other hand, Energy and ViM have no overlap within the specified interval, indicating that the modern detectors are more affected by covariate shift than MSP.
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Table 3: OOD detection performance for ResNet-50 under Gradient-based OOD and Open- Max. OOD detection performance for Open-Set Recognition and gradient-based OOD detection methods on the two semantic shift datasets suggest the same conclusion: MSP baseline best for failure detection and modern methods do not have substantial improvement over baseline once spu- rious covariate shift removed. ODIN, uses T 1000 and e = 0.0014. ODIN uses 1000 with no adversarial perturbations. GradNorm uses T 1.
Goal
Method
OpenImage-O
ImageNet-OOD
MSP
84.0
79.2
ODIN,
86.5
80.1
New-class Detection
ODIN
87.4
80.4
GradNorm
80.4
73.8
OpenMax
87.4
80.2
MSP
89.9
86.8
ODIN,
87.9
83.5
Failure Detection
ODIN
89.3
84.6
GradNorm
77.2
72.2
OpenMax
88.5
83.4
Table 4: Detailed breakdown of AUROC performance with respect to ImageNet-OOD. Break- down reveals that the improvement observed in both Max-Logit and Energy is attributed to bet- ter separation between incorrect ID predictions and OOD predictions, shown by the large margin of increase in AUROC between Incorrect ID (+) vs. OOD (-). Performance between correct distribution predictions and out-of-distribution predictions are similar. Additionally, when evaluat- ing incorrect ID predictions vs. correct ID predictions, separability decreases for Max- Max-Logit and Energy compared to MSP.
(+) vs. (-) Incorrect (+) OOD.()
Correct Incorrect (-)
MSP Hendrycks & Gimpel 2017
86.9
54.7
86.4
Max-Logit (Hearryck yolk 20
61.8
77.7
Energy (Liu 2020
85.4
62.4
76.2
D OPEN-SET RECOGNITION AND GRADIENT-BASED OOD DETECTION
Open-Set Recognition (OSR) a similar task to semantic OOD detection, where the goal to identify unseen classes. Gradient-based OOD detection algorithms uses some aspects the gra- dients a model to calculate an OOI) score. Since detection requires the calculation gradient on each image, such method of OOD detection is more computationally expensive. We perform the same analysis from 4.3 but on a single ResNet-50 model on the popular OSR algorithm Open- Max (Hsu et al. 2020 which does not require any retraining and gradient-based OOD detection algorithm ODIN (Liang et al. 2018) and GradNorm (Huang et al.) (021)) on Openlmage-O and ImageNer-OOD, Results from Table I reveals the same consistent behavior where we see substan- tial improvement from MSP on OpenTmage-O but not ImageNet-OOD for new-class detection, and MSP baseline outperforms these methods for failure detection.
E ADDITIONAL EXPERIMENTS FOR DETECTION OF INCORRECT EXAMPLES
quantitatively examine AUROC on ImageNet-OOD to measure the separability between three groups of examples: correctly classified in-distribution (ID) examples, incorrectly classified ID examples, and semantic shifted OOD examples.
In addition to the analysis that visualizes the ranking of OOD scores in Section 43
Specifically, since AUROC is measure of separability (i.e. the probability of scoring positive example higher than a negative example), we examine the AUROC among these three groups of examples. Using the probabilistic interpretation of AUROC, we can decompose AUROC between
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ID OOD by correct ID and incorrect ID using the law of total probability:
(((m))) fx
((f())) fzout) [C (z)( = 1)+
((f()))) (xoot) ) [C((m:)) ()p(C( = 0)
where Tin, Tout refer to semantic ID and OOD examples, respectively, f is the OOD scoring func- tion, and C is indicator function with C(x) = 1 if is predicted correctly, and otherwise.
Table reports (((( > ((Eooa))C(aaa = 1) (i.e. column Correct ID (+) vs. OOD (-)) and ((((m)) [CIont))C(rrn) 0) (i.e. column Incorrect ID (+) OOD (-)). The results reveal increased separability between incorrect ID vs. OOD from MSP to ViM and ReAct, decreased sep- arability between correctly classified ID vs. incorrectly classified ID, and roughly the same separa- bility between correctly classified vs. OOD. Since p(((m = 1) in Equation 2 is the classification accuracy on the dataset, we see that for 71. 71.6% of increase in new-class detection AUROC from ViM can be attributed to an increase in performance of detecting Incorrect ID vs. OOD predic- tions. This pattern supports the claim that many advanced OOD detection methods improve under the old benchmark by detecting more incorrectly classified examples as ID rather than balanced improvement across the ID set.
Additionally, we also use AUROC to approximate the probability of scoring correct ID higher than incorrect ID examples, reported in the Correct ID (+) vs. Incorrect ID (-) column in Table 4 We found that advanced OOD detection methods have lower separability between correctly classified ID vs. incorrectly classified ID). Having lower separability between correct vs. incorrect hurts the performance of the model from safety perspective, more problematic examples can pass through the OOD detection filter.
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OOD Detection on Random Models with DenseNet
OOD Detection on Random Models with Wide ResNet
IN-C (Natse)
IN-C (Nesee)
8'
AUROC
0.6
AUROC
0.6
L
MSP
Ma agitt
Energy
Mahaalanbbi
ViM
KNW
Readt
Mex-Lagt
Energy
Matatenoble
VIM
Roait
Figure 11: OOD detection performance under random models. AUROC performance of 5 DenseNet- 121 models (left) or Wide ResNet-50 model (right) with random, untrained param- eters on subsets of ImageNet-C (Hendrycks & Dietterich] 2019) under the existing benchmark vs our proposed benchmark. Colors indicate the specific random model and the markers indicate corruption type. Results reveals the same conclusion as the ResNet-50 sanity check.
SANITY CHECK EXTENDS TO OTHER MODEL ARCHITECTURES
We expand our analysis of the sanity check in Section to other model architectures and to the scenario of failure detection. For architectures, we display additional random DenseNet- 121 (Huang) al. 2017) and Wide ResNet-50 (Zagoruyko & Komodakis 2016). Results from Figure [1 reveal that the same issue with sanity check occurs on DenseNet, except ViM, and Wide Resnet-50 in the scenario of new-class detection, suggesting that this issue applies to convolution based architecture.
G QUALITATIVE ANALYSIS ON COVARIATE SHIFT BIAS
We expand our analysis by visualizing images where modern OOD detection algorithms and the baseline MSP differ the most. We perform this analysis on ImageNet-IK, ImageNet-OOD, OpenImage-O, and Imagehet-Sketch on a ResNet-50 model trained on ImageNet- We found that modern OOD detection algorithms ViM and ASH-B tends to latch on to specifie spurious fea- tures that are exacerbated by dataset with uncontrolled covariate shift, confounding their evaluation on semantic shift.
We reveal Figure 12 that the OOD detection method ViM tends to give texture-like images low scores, detecting them OOD. This effect more prominent OpenImage-O because it appears there are many images in a different domain. In contrast, our dataset ImageNet-OOD has images more similar ImageNet-IK. The observed change in domain is a clear example of unnecessary covariate shifts, explaining the vanishing performance gains of ViM in ImageNet-OOD: Similarly Figure [3] we observe that the OOD detection algorithm ASH-B tends to score text-like images as OOD. Though there are some imperfections due to the class semantics (e.g. map is an object but also can be a diagram), ImageNet-OOD overall still produced more similar looking images to ImageNet-IK.
In summary, qualitative analysis on the discrepancy between modern OOD detection algorithms and MSP motivate our dataset: the minimization of covariate shift is important when assessing an OOD detector's 's performance on semantic shift for the task of nlw-class detection.
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ImageNet-1k
ImageNet-OOD
Openlmage-0
ImageNet-Sketch
Figure 12: Images with highest discrepancy in ranks between ViM and MSP. We show 12 im- ages from ImageNet-IK, ImageNet-OOD, OpenImage-O. and ImageNet-Skeete with the highest discrepancy in ranks where ViM scores low (OOD) and MSP scores high (ID) revealing that ViM tends prefer detecting texture OOD. The images also reveal that ImageNet-OOD images are more realistic and comparable to ImageNet- 1K than OpenImage-O.
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ImageNet-1K
ImageNet-OOD
Openimage-O
3pm
salt
Comme
This an excelor leve
This CV words This excelent lev
This an excelon leve
20 MPH speed Smmlt
ist PORE PRODUCTION Mre contu
24.3
Thee Pellght
The Digihs
ImageNet-Sketch
Ross
Figure 13: Images with highest discrepancy in ranks between ASH-B and MSP. We show 12 images from ImageNet- IK, ImageNet-OOD, OpenImage-O,, and ImageNet-SSetch with the highest discrepancy in ranks where ASH-B scores low (OOD) and MSP scores high (ID) revealing that ASH-B tends to prefer detecting text as OOD.
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H MORE DETAILS ON IMAGENET-OOD CONSTRUCTION
In this section, we provide details on the manual selection process of ImageNet-OOD, Because im- ages from ID classes may leak into OOD classes human labelers are unable disambiguate two classes, such as "violin" and "viola" (Russakovsky et al. 2015). we manually selected 1000 classes from ImageNet-21K
construct ImageNet- OOD. However, even after excluding hypemnmss, hy- ponyms, and the "organism" subtree, there are still 5074 remaining candidate ImageNet-21K classes. is simply infeasible check all 5074 classes against all 1000 ImageNet- IK classes, which would require 5074 x 1000 5, 074, 000 manual comparisons. Therefore, we need to employ another mechanism that can pass through the 5074 classes in linear time.
To start the collection process, we aim to pick out 1000 classes, We first gathered the sister classes for each ImageNet-IK class. sister class c is defined as a class that shares a direct parent with an ImageNet-IK class ct. For example, the sister classes for the ImageNet-IK class "microwave" has sister classes "rood_processor", 'ice_mas "ice_maker", "hot_plate", "coffee fraferr, and "oven", because these classes all have the same direct parent "kitchen appliance." Considering only sister classes allowed us to further reduce the search space down to 2874 candidate classes
Once had obtained the sister classes, examined the visual and semantic ambiguity between each sister class and its corresponding ImageNet-1K class through example images. Unambiguous classes are added the final list of ImageNet-OOD classes. Since the ambiguity between classes was considered during the curation of ImageNet-IK classes, we assume that there exists minimal ambiguity between classes under different subtrees that contains ImageNet-IK class. This sumption allowed us to only examine the relationship of sister classes with their corresponding ImageNet- 1K class instead of with all 1000 ImageNet-IK classes. In the end, we only needed to compare 13,831 pairs of classes.
Following the manual selection of the 1000 classes, we filtered out the classes with semantically- grounded covariate shift using the method described in Section [ Then, we examined the 1000 images that are the closest to ImageNet-IK validation images in terms ResNet-50 (He et al.) 2016) feature distance filter out visual similarity. We filter out both classes and images. Classes such as "aqualung" (visually similar to ID class "scuba diver") were filtered out this stage, and images with indistinguishable visuals were also thrown out. The final resulting dataset will include 3 3,,007 images from 637 classes.
I IMAGENET-OON CLASSES
Synset ID
Class Name abattoir
Synset
Class Name
n02666943
n04108822
rriddb
n02678897
adapter
n04113406
roulette wheel
n02688273
air filter
n041145 104114844
router
n02689434
air hammer
n04116098
rubber band
n02698634
alpenstock
n04118635
ruin
n02705429
amphora
n04119230
rumble_seat
m02705944
amplifier
n04122685
sachet
n02710044
andiron
n04123740
saddle
65
antiperspirant
n04132603
samisen
n02725872
anvil
n04134008
sandhag
n02726681
apartment apartment_building
n04136800
fash_fattener
n02757337
audiometer
n04139140
saucepot
n02758960
autoclave
nO4150153
scouring -paliin--paaddd
n02763604
aviary
n04150980
scraper
n02767956
backbench
n04167346
seeder
n02768226
backboard
n041681 199
Segway
n02770721
backscratcher
n04171831
semiconductor._evvice
n02770830
backseat
n04176068
serving_cart
n02775897
Bailey bridge
n04176190
serving-dish
n02776205
bait
n04182152
shadow box
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Synset ID
Class Name
Synset
Class Name
n02783994
baluster
n04184435
shaper
n02786331
bandbox
n04186051
shaving cream
n02799323
baseball cap
n04190376
shelf bracket
n02806379
bat
n04198722
shiv
n02807523
bath_oil
n04200258
shoebox
n02807616
bathrobe
n04200537
shochorn
n02808185
bath salts
n04206356
shotgun
n02811611
battle. battle.cruiser cruiser
n04210120
shredder
n02812949
bayonet
n04211219
shunter
n02816656
beanbag
n04218564
silencer
1
17011
bearing
n04219424
silk
n02821202
bedpan
n04221823
simulator
n02823586
beer garden
n04224842
sitar
n02826068
bell jar
n(4228215
ski binding
n02831237
beret
n04228693
ski_cap
n02841187
binnacle
n04233124
skyscraper
n02843553
feeder
nO4237423
slicer
n02851939
blindfold
n04238321
slide fastener
n02855089
blower
n04248851
snare
m02868975
bone-ash cup
n04252331
snowshoe
m02869249
bones
n04252653
snow thrower
n02874442
bootjack
n04253057
snuffbox
n02877266
bottle
n04258333
solar_heater
n02879087
bouquet
n04258859
soldering iron
n02880842
Bowie knife
n04260364
sonogram
n02881757
bowler hat
n04269270
spark_plup
m02882190
bowling alley
n04270891
spear
n02882301
bowling ball
n04272389
spectator pump
n02882647
bowling-pin
n04273285
speculum
n02887489
brace
n04282494
splint
n02890940
brake_shor
n04284869
sport_kite
m02892948
brass knucks
n04287747
spray gun
n02893608
breadbasket
n04289027
sprinkler
n02893941
bread brife_knife
n04290259
spur
n02895438
breathalyzer
n04292921
squeegee
n02903204
broadcaster
n04303357
staple-gun
n02904803
brocade
n04303497
stapler
n02905036
broiler
n04306592
stator
n02910145
bucket seat
n04314914
step
n02911332
buffer
n04315342
hrep-down_transfome
n02925009
bushelbbakket
n04320973
stirrup
n02927764
butter_dish
n04326896
stool
n02928608
button
104331639
straightener
n02939866
caliper
n04335886
streetlight
no2940385
call center
n04344003
stud finder
n02944579
camouflage
n04346157
stun gun
n02947660
canal .oot
n04358117
supercomputer
n02951703
canopic_jat
n04364160
wwnge_suppressor
n02952237
canopy
n04364545
surgical
n02955247
capacitor
n04373894
sword
n02956699
capitol
n04386051
tailstock
n02957008
capote
n04387095
tam
n02960690
carabiner
n04389854
tank Lenkeen
n02960903
carafe
n04392113
tape
n
carboy
n04409128
tender
n02962200
carburetor
n04414675
Tesla coil
n02967782
carpet sweeper
n04417180
textile machine
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Synset ID
Class Name
Synset
Class Name
n02970685
car-seat
n04419073
theodolite
n02973017
cartridge holder
n04421872
thermometer
n02973904
carving knife
n04432662
ticking tin
n02976249
case_knife
n04438897
n02977330
cashmere
n04442441
toaster_oven
n02978055
casket
n04449966
tomahawk
n02981024
catacomb
n04450749
tongs
n02982232
catapult
n04451318
tongue depressor
n02986160
-atteeguard
n04452528
tool_
n02988066
C-clamp
n04453156
toothbrush
n02993199 194
cenotaph
n04453390
toothpick
n02993368
censer
n04469514
trampoline
n02998003
cereal box
n04474035
transporter
n03005033
chancellery
n04477387
treadmill
n03005285
chandelier
n04479939
deestlebrrdge
n03011355
checker
n04482177
tricorn
n03014440
chessman
n04483073
trigger
n03019685
chin_rest
n04483925
trimmer
n03027250
chuck
n04488202
trophy casse
n03029445
churn
n04489817
trowel
n03030353
cigar box
n04495698
tudung
n03033362
circuit
n04495843
tughoat
n03034405
circuitry
n04496872
tumbler
n03041114
cleat
n04497801
tuning_fork
n03049924
cloth.cap
n04502502
tweed
n03050655
clothes dlothes.dryer
n04502851
twenty-two
n03051249
clothespin
n04520784
n03061674
cockpit
n04525821
Venn dinngaa
n03063199
coffee_filter
n04526964
ventilator
n03064758
coffin
n04532831
vibraphone
n03066359
coil-spring
n04533199
vibrator
nO3067093
cold cathode
nO4538552
vise
n03075097
comb
n04540255
volleyball net
n03080633
compass
n04554406
washboard
n03082807
compressor
n04556408
watch.cap
n03087069
concrete_min _mixer
n04559166
water_cooler
n03089753
conference_centeee
n04568069
weathervane
n03097362
control center
n04568841
webbing
n03099147 147
convector
n04579056
whiskey bottle
n03101664
cookie_jar
n04582869
wicket
n03103563
coonskin.cap
n04586581
winder
no3 105088
copyholder
n04589325
window_bor _bx
n03105467
corbel
n04590746
Windshield.wippg
n031 15410
cotton_flamm flannel
n04594489
wire
103119510
coupling
n04606574
wrench
n03122073
covered bridge
n04613939
Zamboni
n03133415
crock
n04615226
zither
n03 10344043
cruct
n06275095
cable
n03 141702
crusher
n07579688
n03150232
curler
n07579917
adobo
n03156767
cylinder
n07580359
casserole
n03158885
dagger
n07591961
paella
103161450
damper
n07593471
viand
n03175457
densitometer
n07594066
cake_mix
n03176386
denture
n07607138
chocolate
n03176594
deodorant
n0761199 1991
mousse
n03180504
destroyer
n07613815
jello
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Synset ID
Class Name
Synset
Class Name
n03219135
doll
n07617708
plum-pudding
n03229244
dowel
n07617932
corn fadduuuddngggg
n03232543
drain basket
n07618119
duff' chocolate.pudding
n03233744
drawbridge
n07618432
nO3235327
drawknife
n07621618
garnish
n03235796
drawstring.
n07624466
turnover
n03240892
drill press
n07641928
fish cake
n03247083
dropper
n07642361
fish stick
n03249342
drugstore
n07648913
buffalo. __in
n03250279
drumhead
n07648997
barbecued. wirn
n03253796
duffel
n07654148
barbecue
n03254046
duffel_coat
n07654298
biryani
n03254862
dulcimer
n07655263
saute
n03255899
dumpeart
n07665438
veal parmesan
n03256166
dump_truck
n07666176
veal_cordon _leu
n03261776
earphone
n07680313
bap
n03266371
eggbeater
n07680517
breadstick
n03267468
ejection seat
n07680761
bread
nO3272125
hammer
n07681450
challah
n03282295
embassy
n07681691
cinnamon bread
nO3287351
energizer
n07682197
crouton
n03293741
equalizer
n07682316
n03296081
escapement
n07682477
English_muffin
n03309356
eyepatch
n07682624
flatbread
n03326795
felt ferry
n07682808
garlic. bread
n03329663
n07684164
matzo
n03331077
fez
n07684517
raisin bread
n03342127
finger-painting
n07685730
rye_bres rye_bread
n03345837
tre_eextnn
n07686720
sour_bread
n03350204
fishbowl
n07686873
toast
n03351434
fishing-geen -gear
n07687053
wafer
nO3356982
flannel
n07696527
butty
n03359566
flask
n07696625
ham sandwich
n03363749
flintlock
n07696728
nO3364599
float
n07696839
sandssh
n03367410
florist
n07696977
open-face_sandwich
n03367545
floss
n07697699
Sloppy
n03378174
food processor
n07697825
bomber
n03379828
footbridge
n07698250
gyro
n03392741
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30


Reviews:
[{"review_id": "oV0DJN6fwm", "reviewer": "Reviewer_JbD1", "summary": "This paper analyzes the capabilities of current OOD detection algorithms highlighting a bias towards covariate shifts. In consequence, the authors introduce ImageNet-OOD, a dataset that can assess the impact of semantic shifts without the influence of covariate shifts. \n\nThe authors address the limitation of current benchmarks to correctly evaluate out-of-distribution (OOD) detection algorithms due to a missing clear separation between semantic and covariate shifts. \n\nFor the proposed set, the corresponding in-distribution (ID) dataset is considered to be ImageNet-1K. \nImageNet-OOD is a manually curated dataset, with 31807 images from 637 classes. \nWhen building ImageNet-OOD, the authors started from ImageNet-21K and curated it in order to address the following issues:\n   - semantic ambiguity\n   - visual ambiguity\n   - unnecessary covariate shifts \n \nCuration steps:\n   - **1 All ImageNet-1K classes, their hypernyms, and hyponyms** - remove classes corresponding to ImageNet-1k classes, their hypernyms and hyponyms  \n   - **2 Hyponyms of \"Organism\"** - as there is an issue with the classification of natural beings in WordNet (classified by both technical biological levels and non-technical categories), all classes having 'organism' as a hyponym are removed \n   - **3 Semantically-grounded covariate shifts** - remove potential 'semantically-grounded covariate shifts'. If we train a binary classifier to differentiate between 'dog' and 'vehicle', it can also be understood as an 'animal' vs. 'vehicle' classifier => class 'cat' can be considered as a semantically-grounded covariate shift  and should be removed to avoid confusions. Considering each pair of classes from ImageNet-1K, the common ancestor is identified, and the classes are associated with the immediate descendants of this common ancestor (most general description). Further, exclude all classes from ImageNet-21K that are hyponyms of the general descriptions identified above\n   - **4 Final Class Selection** - remove samples generating semantic ambiguity due to inaccurate hierarchical relations in ImageNet labels (e.g. violin and viola)\n\nThe authors perform extensive experiments on seven OOD detection algorithms across 13 network architectures highlighting that: \n - OOD detection algorithms are more sensitive to covariate shifts than semantic shifts \n- the practical benefits of new algorithms vs. MSP (maximum softmax probability) disappear under both new-class detection and failure detection scenarios", "questions": "**Q1** When evaluating the sensitivity to covariate or semantic shift, apart from the distances towards the closest ImageNet-1K sample (Fig. 3 left), you can also consider distances between the considered datasets (e.g. OTDD [2])\nThis can help you understand if the sensitivity can be explained by dataset statistics or is simply a model / method bias. \nHave you considered such an analysis?\nFor example, in Table 1, the model trained on ImageNet-1K sees samples from ImageNet-C and ImageNet-OOD. Depending on the distances between those sets and ImageNet-1K, we can understand why covariate or semantic shifts are captured by the OOD detection algorithms.\n\n**Q2**  If we first perform the third curation step \"Semantically-grounded covariate shifts\", is there any reason to employ step 1 \"All ImageNet-1K classes, their hypernyms, and hyponyms\"? \nBy reaching the most general decision boundary for each pair of ImageNet-1K classes and further removing all the classes that fall under those broader decision boundaries from ImageNet-21K you remove both hypernyms and hyponyms. \n\n**Q3** Regarding the example with 'viola' and 'violin' from Figure 1. Is there any reason for this ambiguity to persist after the elimination of 'semantic-grounded covariate shifts'?\n\n**Q4** For the 'Final Class Selection' step, have you considered the implementation of an automated process that exploits, for example, the CLIP embeddings? \nInstead of manually searching for those ambiguities, you can use CLIP zero-shot to classify images in both ID and OOD classes and understand potential similarities / confusions. \nThis would be useful for having a receipt for curating OOD sets based on a considered ID set, without requiring human intervention.  \n\n**Q5** Regarding examples from Figure 2: Images with similar visual contents are presented - this means that a certain threshold for visual similarity is considered when removing visually similar classes. How do you choose this threshold, considering that the selection is manually performed. Regarding **Q4**, it would be useful to use such an automatic approach in order to establish a relevant threshold. \n\n**Q6** Regarding Sec. 4.2 - The considered covariate shifts are restricted to image alteration techniques (blur, noise, etc.). But, covariate shifts can also appear when we observe objects in city backgrounds in ID, while in the OOD set, we observe objects on a forest background. \n\n*Q6.1* Have you considered this type of covariate shifts? I assume that the conclusion of this section will not hold in this scenario. Actually, it may be valid for a restricted set of covariate shifts, strongly related with the inductive bias of the considered model architecture. \n\n*Q6.2* Have you performed the same analysis considering semantic shifts? This would be useful in order to conclude that random models are more sensitive to covariate shifts.  \n\n[2] Alvarez-Melis and Fusi \"Geometric dataset distances via optimal transport\" -NeurIPS 2020 \n[3] Radford et al. Learning transferable visual models from natural language supervision - ICML 2021", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "**S1** Introduce ImageNet-OOD, a curated OOD dataset for ImageNet-1K. \n\n**S2** Highlighting the ambiguity between semantic and covariate shifts for the OOD detection problem. \n\n**S3** Extensive experimental analysis, considering 7 OOD detection algorithms and 13 model architectures.", "weaknesses": "**W1** Introducing separate datasets for assessing semantic and covariate shifts is relevant for roughly understanding OOD detection capabilities. \nYet, in a real-case scenario, both semantic and covariate shifts will be present, and whether we wish to ignore one of the two should be specified through the training set (e.g. using a multi-environment setup - see [1]) \nIf both semantic and covariate shifts are to be considered, then it is expected that the OOD detection algorithm will first identify the one generating the highest shift.\n(see question **Q1**)\n\n**W2** The curation steps can benefit from an in-depth analysis\nSee questions **Q2**, **Q3**, **Q4** and **Q5**\n\n**W3** Conclusion of Sec.4.2, where randomly initialized models are considered for testing OOD detectors. Here, the authors conclude that there is a bias towards detecting covariate shifts even for untrained models. \nThis observed bias is most probably justified by the inductive bias of the considered ResNet-50 model. \nRandomly initialized models are more sensitive to specific covariate shifts, but this observation cannot be generalized to any covariate shift. (see question **Q6**)\n\n\n[1] Smeu et al. Env-aware anomaly detection: Ignore style changes, stay true to content! - NeurIPSW 2022", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "ATlNV96bnh", "reviewer": "Reviewer_PDzS", "summary": "This paper introduces a new out-of-distribution (OOD) task dataset, primarily composed of data from ImageNet-1K and ImageNet-21K. The authors selected a portion of the data using specific rules and meticulous manual annotation for OOD tasks. The authors discovered that many state-of-the-art (SOTA) methods performed worse than certain baseline methods on this dataset, prompting further reflection on OOD tasks.", "questions": "As shown in the weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The motivation of this article is meaningful. Currently, available OOD detection datasets do have certain issues, and by using a more carefully selected dataset, it is possible to define the problem of OOD detection better.\n\n2. The dataset proposed in this article has inspired the development of OOD detection tasks. The authors discovered that many state-of-the-art methods did not perform well on their dataset, and based on this, they made their findings, which are beneficial for further research.\n\n3. The description in this article is clear, making it easy for readers to understand the characteristics of the dataset, its construction method, experimental results, and conclusions.", "weaknesses": "1. The focus of this paper is primarily on the dataset and analysis, which are undoubtedly meaningful aspects. However, the author fails to provide their own methods to improve the effectiveness of OOD detection tasks, which results in a lack of depth and contribution in this paper.\n\n2. The author's comparison methods lack some of the latest approaches. In recent conferences such as CVPR 2023, new methods have been proposed. Including these methods in the comparison would make the article more comprehensive. Additionally, many methods may be sensitive to hyperparameters, so it would be beneficial to discuss the adjustment of hyperparameters when changing to new datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "YKaU6pdpFa", "reviewer": "Reviewer_YNoR", "summary": "This paper introduces ImageNet-OOD, a new out-of-distribution (OOD) detection dataset that minimizes covariate shift compared to ImageNet-1K by manually selecting semantically different classes from ImageNet-21K. Using this dataset, the authors evaluate several recent OOD detection algorithms and find they offer little benefit over the maximum softmax probability (MSP) baseline for detecting semantic shifts. The key conclusions are:\n1. Modern OOD detectors are much more sensitive to covariate shifts than semantic shifts.\n2. On ImageNet-OOD, modern OOD detectors offer <1% AUROC improvement over MSP for new-class detection.\n3. For failure detection, MSP still outperforms modern OOD detectors on ImageNet-OOD.\n4. The benefits of modern OOD detectors come more from better separating incorrect in-distribution examples rather than improving on semantic shifts.", "questions": "No more questions.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. ImageNet-OOD appears to be a useful benchmark for evaluating OOD detection methods on semantic shifts. The careful data curation process is commendable.\n2. The analyses on the susceptibility of modern methods to covariate shifts are insightful. The experiments are comprehensive across different datasets, algorithms, and metrics.\n3. The finding that MSP remains a strong baseline is an important result for the OOD detection community. It helps calibrate expectations on recent progress.", "weaknesses": "1. While covariate shift robustness is desirable, the goal of semantic shift detection is also useful in many applications like open set recognition. The heavy focus on covariate shifts undervalues semantic shift detection.\n2. More analysis could be provided on the characteristics of examples that lead methods to confuse covariate and semantic shifts.\nThe writing and organization needs polish in some areas. The high-level conclusions could be stated more clearly in the intro and abstract.\n3. In summary, this is an reasonable contribution introducing a new dataset and providing useful experiments analyzing modern OOD detection methods. I suggest acceptance after revisions to clarify the presentation and provide additional analysis/discussion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "61SpzEy66K", "reviewer": "Reviewer_pNua", "summary": "This work introduces a new OOD detection benchmark for ImageNet-1K, namely ImageNet-OOD (IN-OOD for short). Compared to existing benchmarks (e.g., Species, OpenImage-O, SSB, NINCO), IN-OOD minimizes covariate shifts and operates at a large scale (in terms of # OOD categories and # images). With this new benchmark, the authors evaluate several recent post-hoc detectors under both 1) novel class detection and 2) failure detection schemes. The major finding is that existing detectors are (undesirably) much more sensitive to covariate shifts than semantic shifts, and most detectors do not provide practical benefits over the simplest baseline MSP.", "questions": "As demonstrated by NINCO work, human inspection is necessary for constructing clean OOD datasets (which is also recognized in this work). However, human inspection could be extremely costly, which I believe is the reason why NINCO itself is limited in size (a few hundreds or at most thousands of images). IN-OOD in comparison has a total of 31,807 images. I was wondering how thorough and rigorous the human inspection was for the \"final review\" of IN-OOD at this size. How many human inspectors were involved and how long did it take for the final review?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Although not for the first time, this work does focus on and point out several crucial issues for OOD detection research (which unfortunately still haven't been paid enough attention by the researchers in this field). These issues include: 1) the lack of a semantic shift-only, clean, and large-scale OOD dataset for ImageNet-1K, 2) the sensitivity of existing methods to covariate shift, and 3) the mismatch between a) the ultimate goal of OOD detection and b) the \"wrong\" goal reflected by the current evaluation which is the result of issue 1) and 2).\n2. The constructed IN-OOD would indeed be valuable, especially considering the rigorousness introduced by the several filtering processes and the final human inspection. I could imagine how big the human efforts involved in this process are, and I personally appreciate it.\n3. Performing evaluations under both new-class detection and failure detection setting is good, which can provide a unified assessment.", "weaknesses": "1. An important reference, OpenOOD v1.5 [1] is missing (released on arXiv in June 2023). Their evaluation results in the full-spectrum OOD detection setting (considering semantic-shifted and covariate-shifted samples together [2]) are also concrete evidences that current OOD detection methods, not restricting to post-hoc methods, are very sensitive to covariate shifts. This is actually presented as one of their major observations, and thus I believe it is necessary to discuss this work at least in the Related Work section.\n\n2. Like I said, I appreciate the efforts in constructing IN-OOD and I recognize the value in this new dataset. However, I wouldn't say that the observation of \"OOD detectors are more sensitive to covariate shifts than semantic shifts\", which is one of the claimed contributions, is new. Evidences include both [1, 2]. Another major observation of this work, \"the practical benefits of these algorithms disappear under new-class detection\", is also similar to one presented in [1], where they find the improvements in \"near-OOD\" (which essentially has less covariate shifts than \"far-OOD\") detection is limited.\n\n3. This is not really a weakness. I like the example in Figure 1 where \"Animal, Vehicle\" are ID, and training images of \"Animal\" is dog while test images could be cat. This actually points to a type of covariate shift called Subpopulation Shift [3]. I think the discussion on this example could be made more clear by explicitly discussing the relationship between OOD detection and Subpopulation Shift (although I agree that this could often times be application-dependent).\n\n4. Lastly, again this is not technically a weakness, but part of me feels that this work might suit dedicated dataset & benchmark track better.\n\n[1] OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection\n\n[2] Full-Spectrum Out-of-Distribution Detection\n\n[3] BREEDS: Benchmarks for Subpopulation Shift", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (poster)