Decomposing Texture and Semantics for Out-of-distribution Detection
Authors: Jeong-Hyeon Moon
Namhyuk Ahn
Kyung-Ah Sohn
Venue: ICLR 2022 Submitted (2022)
Keywords: Out-of-distribution detection
Fourier analysis
Normailzing flow model

Abstract: Out-of-distribution (OOD) detection has made significant progress in recent years because the distribution mismatch between the training and testing can severely deteriorate the reliability of a machine learning system.Nevertheless, the lack of precise interpretation of the in-distribution limits the application of OOD detection methods to real-world system pipielines. To tackle this issue, we decompose the definition of the in-distribution into texture and semantics, motivated by real-world scenarios. In addition, we design new benchmarks to measure the robustness that OOD detection methods should have. To achieve a good balance between the OOD detection performance and robustness, our method takes a divide-and-conquer approach. That is, the model first tackles each component of the texture and semantics separately, and then combines them later. Such design philosophy is empirically proven by a series of benchmarks including not only ours but also the conventional counterpart.

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DECOMPOSING TEXTURE AND SEMANTICS FOR OUT-OF-DISTRIBUTION DETECTION
Anonymous authors Paper under double-blind review
ABSTRACT
Out-of-distribution (OOD) detection has made sigmificant progress in recent years because the distribution mismatch between the training and testing can severely deteriorate the reliability of a machine learning system. Nevertheless, the lack precise interpretation of the in-distribution limits the application of OOD detec- tion methods to real-world system pipielines. To tackle this issue, we decompose the definition of the in-distribution into texture and semanties, motivated by real- world scenarios. In addition, we design new benchmarks to measure the robust- ness that OOD detection methods should have. To achieve a good balance between the OOD detection performance and robustness, our method takes a divide-and- conquer approach. That is, the model first tackles each component of the texture and semantics separately, and then combines them later. Such design philosophy is empirically proven by series of benchmarks including not only ours but also the conventional counterpart.
In-distribution manifold
In-distribution manifolds
Training
Semantic driven in-distribbtion
Texture driven in-distribution
Textural discrapancy
Semantic discrepency
(a) Traditional definition of the in-distribution
(b) Decomposed definition the in-distribution
Figure 1: How to define the in-distribution? (a) Traditional aut-of-distributin detection studies have managed the in ii-iitributtinn in an entangled view. However, this assumption could be naïve considering the complex nature of the real environment. (b) We decompose the definition of the in- distribution to the texture and semantic aspects. This provides the flexibility to handle complicated scenarios by determining which definition of the in-distribution is suitable for given scenario.
INTRODUCTION
The out-of-distribution (OOD) detection is the task that recognizes whether the given data comes from the distribution of training samples (also known as in-distribution) not. Any machine leamiig-based system could receive input samples that have a completely disparate distribution from the training environments (e.g., dataset). Since the distribution shift can severely degrade the model performance (Amodei et al. (016). is potential threat for reliable real-world Al system.
However, an ambiguous definition of the "in-distribution" limits the feasibility of the OOD detection method in real-world applications, considering the various OOD scenarios. For example, subtle cor- ruption is a clear signal of the OOD in the machine vision field while change in semantic informa- tion might not be. On the other hand, an autonomous driving system may assume the in in-distribution from the semantic-oriented perspective; e.g., unseen traffic sign is the OOD. Interestingly, the interpretations of the in diddtributiio described in the above scenarios are not correlated; rather, they contradicted. Unfortunately, most of the conventional OOD detection methods (Zhang et al.

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Tack et al. 2020: Ren al. 2019) assume the in-distribution single-mode, thus not able to handle other aspects of the OOD (Figure [T].
To tackle this issue, we revisit the definition of the in-distribution by decomposing it into two dif- ferent factors: texture and semantics (Figure IT)) For the texture OOD case, we define the OOD as the textural difference between the in- and out-of-distribution datasets. On the contrary, the semantic OOD focuses on the class labels that do not exist in the in-distribution environment, Note that the two aspects have trade-off relationship, thus detecting both OOD problems with single model is challenging with the (conventional) entangled OOD point of view.
Similar ours, Geirhos et al. (2018) investigated the texture-shape cue conflict in the network, and a series of following studies (Hermann et al. 2019 Li et al. 2020 Ahmed & Courville 2020) explored the way to find a balance between these perspectives. However, aforementioned works utilize texture-shape to analyze the bias inherited in deep networks. In this work, instead, we focus on analyzing the texture and semantic nature underlying the in-distribution build a more practically applicable OOD detection method.
However, to the best of our knowledge, none of the studies on the OOD detection benchmark have thoroughly investigated the definition of the in-distribution. This can be problematic when the OOD detection method judges the image corrupted by minor distortion as OOD, even when the envi- romment is tolerant to the small changes in texture. Because of such complicated scenario, it is important to evaluate the OOD detection method in a comprehensive way that goes beyond the sim- ple benchmarks. In this work, method according to the decomposed definition of the in-distribution. One notable observation in our benchmark is that most of the previous OOD detection methods highly biased the texture information and ignore the semantic clues in many cases.
propose new approach to measuring the performance of the
To mitigate this issue, our method tackles the texture and semantic information separately and ag- gregates these at the final module (Figure 2 To effectively extract the texture information, we use the 2D Fourier transform motivated by the recent frequency domain-driven deep method (Xu al. 2020). For the semantic feature, we design an extraction module upon the Deep-SVDD (Rulf et al. 2018 with our novel angular distance-base
initialization strategy. We then combine two features using the normalizing flow-based method (Dinh et al.) 2016), followed by our factor control mech- anism. This control module provides the flexibility to handle various OOD scenarios by choosing which decomposed feature is more important in the given surrounding OOD circumstance.
The main contributions of this work are as follows:
We decompose the "unclear" definition of the in-distribution into texture semantics. To the best of our knowledge, this is the first attempt to clarify the OOD itself in this field.
Motivated by real-world problems, we create new OOD detection benchmark scenarios to evaluate the models based on the decomposed in n--iistrbuttin factors.
We propose novel OOD detection method that is effective both texture & semantics as well as the conventional benchmark setups. In addition, our method does not require any auxiliary datasets class labels unlike the previous models.
RELATED WORK
In this section, we briefly overview the notable studies on the OOD detection field. We categorize the deep learning-based OOD detection methods into three groups based on the characteristics of the information that they used.
Class labels of the in-distribution. Early studies on deep 00D methods rely on class supervision. ODIN, Generailized ODIN (Liang et al. 2017 Hsu et al. 2020) use the uncertainty measure derived by the Softmax output. It determines the given sample the OOD when the output probability of all classes is less than the predefined threshold. (asstyy & Oore (2020); Lee et al. (2018) utilize the extracted feature map (e.g., gram matrix) from the pre-trained networks to calculate the OOD score.
Auxiliary distribution. Outlier exposure (OE) (Hendrycks et al., 2018) exploits additional datasets that disjointed from the test dataset guide the network better representations for OOD)
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ET
ReaINVP
T(x)
(x))
2D DFT
PCA
RealNVP
logp logp (())))))((
X
ResNet
Multi-sVÖD
PCA
S(x)
Es
Figure 2: Model overview. Our framework first extracts the texture and semantic information with the corresponding modules, and then combines them via the normalizing flow-based method. (a) Texture feature T(x) is distilled by the Fourier spectrum-basee component. (b) We use multi- SVDD method with novel angular initialization extract the semantic information (ar). (c) Out- put features are merged by the explicit probability inference method, RealNVP. Here, we introduce the user control parameter À to determine which feature is more suitable for given OOD scenario.
detection. Papadopoulos et al. 22221 further improves the performance of OE by regularizing the network with the total variation distance the Softmax output.
Data augmentation. Recently, contrastive learning- Beanning-based based methods have shown remarkable success on the tasks related to visual representation (He al. 2020: Chen al., 2020). Motivated by this, several studies employ data augmentation methods such image transformation or additional noise
on the OOD detection task (Hendrycks et al. 2019 Tack al. 2020).
Unlike the prior studies that exploit additional information other than the in-distribution, we only utilize the given (in-distribution) training dataset. In addition, we separate and clarify the assumption of the OOD as texture and semantics to improve the practicability in the real world.
3 METHOD
In this section, we present an overview of our proposed method (Section and the feature extrac- tion modules of the model (Section 3.3 and 3.2p. Finally, we introduce the normalizing flow-based conditional probabilistic modeling component (Section 3.4p. Conventional OOD detection has an as- sumption that in-distribution data sampled from the distribution of the training dataset,æ: Pdata- We decompose the image with two factors and calculate the anomaly score based on each factor's likelihood. The texture information T(x) extracted rigorous Fourier analysis process from input x. The semantic information S(x) extracts the content label features such as shape. Our framework calculates the likelihood of these two factors and then combines these likelihoods. Since Normalizing flow model that trains the exact likelihood, the extracted information is adjusted using a lambda that the user can control.
the
3.1 MODEL OVERVIEW
We aim train our method with the decomposed in-distribution likelihood, p(T()) and (((())( (Figure Z. With the given input image æ, we extract the features for each variable with different approaches. In detail, we distil the information from the texture and semantic information with ((x) and Six),, respectively.
The extracted features are combined by the controllable normalizing flow method. Since our normal- izing flow-based model explicitly calculates the negative log-like ligllikelihood, we model each extracted information as log pa (T<x) and log p. (Sxx)zz) where 0 and 6 are trainable parameters of the networks. In addition, we introduce the control parameter À E [0, 1] to model the final probability as À log p(= + (1 - A) log [ogp(x}((xr)) With this control mechanism, a user can determine the appropriate model "mode" by referring to the prior knowledge. For example, in the case where the texture information overwhelms the semantic one for detecting OOD, we can overweight À for the À value of 0.5 (no prior knowledge).
better performance. By default,
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Center vector
In-distribution
In-distribution
OOD sample
sample
Angular intislizatio
K-means based initialization
(b) Angular distance initialization
Figure 3: Comparison on the initialization strategy in multi-SVDD. (a) The model with K-means initialization is effective for the anomaly detection but not for the OOD detection scenario. This is because the definition of the anomaly is the samples that do not belong to the cluster regions (dark shade), while the definition of the OOD is the samples that does not lie in the in-distribution manifold (light shade). (b) Our proposed angular distance-based initialization guides the initial center vectors be positioned following (virtual) circular line. As result, it prevents the OOD samples from not belonging to the in-distribution by creating tight cluster layouts without a hole.
3.2 EXTRACTING THE SEMANTIC INFORMATION
Multi-SVDD. Beyond the one-class anomaly detection that considers the normal data as a single class (e.g., DeepSVDD (Ruff et al. 2018)), recent studies have viewed the normal data as the union of the multiple hidden semantic information (Ghafoori & Leckie, 2020 Park et al. (021). Inspired by this idea, we use the mult method to extract the semantic information in an unsupervised manner for the OOD detection task.
Multi- embeds the samples to the multiple center vectors as close as possible. Suppose the set of center vectors C fci..... CK) } is initialized via K-means and the radius of each center is r = [ri,... rx]. In multi-SVDD, the objective function is defined as follows.
min W.r
Σ k
U Σ max {0, (1o(;;;; W) c31 " 11W113
(I)
Here, (b;;; W) is the deep network with set of weight parameters W and ej is assigned to o(4; W). As the set r is decreased, the samples are condensed into the center vectors. By using the distance between the center vectors and the samples, we get an anomaly score.
Angular distance initialization. The SVDD method is originally introduced for the anomaly de- tection task. Because of the disparity between the OOD and the anomaly detection scenarios, direct application of the WVDD-based model to OOD detection causes unexpected performance degrada- tion. In anomaly detection, even though the abnormal samples lie in the mddistribution manifold, is possible to detect them as abnormal unless they are close to the center vectors cj. For example, as shown in Figure 3al the OOD samples (red) that are located inside of the in-distribution manifold (light blue shade) can be detected as abnormal since they are outside of the tight cluster boundary (dark blue shade). Because of such characteristics, a mixture of Gaussian probability density is a reasonable density space for the anomaly detection model.
Unlike the anomaly detection task, the definition the OOD detection task to find the samples that are not the "in-distribution". In Figure 3al all the OOD samples placed in the in-distribution manifold (light dark shade) are recognized as the in-distribution. To tackle this issue, we propose an angular distance-based center vector initialization strategy:
Ck
|v|l
VERA VER^~N(O. N(D,
(2)
where h is the dimension of the embedding space and 7 is the syper-parameter for the radius of the sphere. After (ææ; W) is trained based on angular initialization, semantic features are extracted through this model; S(x:) = 6(x;; W). By setting the value large enough, we ensure that all sample data are within a radius of the sphere as illustrated in Figure 3b While Equation I drives the training samples to be embedded around the center vectors the sphere, the OOD samples remain near the origin. This embedding space may be weak to recognize the semantic label a given sample, but it sufficient identify whether the sample is OOD not.
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3.3 EXTRACTING THE TEXTURE INFORMATION
To effectively extract the texture property of the in-distribution, we interpret the image in the fre- quency space. With given input image R first convert it into the frequency domain using Discrete Fourier Transform (DFT) F as shown below.
1 ΣΣ p=0 q=0
Fy
1(p,g) e 22a(/a + Sya/w),
hve
Here, [p..q) q) denotes the pixel value of the image at the (p, q) ccoordinatee and F(fes fu) is the output of the DFT at the Cartesian coordinate (fz. fy) in the frequency space. In order to construct scale and rotation invariant frequency information in 2D image, we modify the coordinate system from Cartesian (fz, fy) to polar ()r following Dzanic al. (2019)
53 fr V {(m² + nz)
=
0 = atan2 (fys
(4)
Since directly computing the polar coordinate is computationally expensive and tricky, we iteratively calculate the rotation invariant frequency feature. To do that, we only utilize the first channel of the image as x E R Rixxx and assume that the image is square (i.e., h = w). Let T(x) E R2/w w be the texture feature vector of the image æ. Then, the i-th element of the feature T, (z) calculated as:
Tx = R²- R1-1
Ra Σ
Fofesfo), Σ
Ro F(0.0)
(S)
Discussion. We compare the power spectrum den- sity (PSD) of CIFAR-10 (Krizhevsky et al., 2009), ,
CIFAR-10
w/ comuptti
CIFAR-100 100
distorted CIFAR-10 and CIFAR-TO0 (Krizhevsky al. 2010) datasets (Figure 4). The PSD dis- crepancy between the corrupted CIFAR-I0 and the vanilla one indicates that the image feature acquired from the frequency domain is adequate to represent the texture cue. In contrast, CIFAR-10 and CIFAR- 100 are not distinguishable in the frequency domain since they have very similar image texture due the small resolution. These observations support our as- sumption that the OOD detection model should de- couple the texture and semantic information to prop- erly handle the uncorrelated cues.
C10
C10 w/ corruption
C100
10
Figure 4: Power spectrum density analysis.
3.4 FEATURE COMPOSITION VIA NORMALIZING FLOW
Since we design our framework directly sample the probability, any ad-hoc scoring functions not required. Instead, we use normalizing flow-based method (Dinh al.] 2014 Rezende & Mohamed 2015; Dinh al.] (016) that uses the probability of given samples as a loss function. In the following, we will describe how to get the probability of samples from the prior probability (Normal distribution) using normalizing flow.
Given sample æ, normal prior probability distribution pz on a latent variable Z, and bijection f:X- Z (with = f ). the change of variables defines a model distribution on X by
@f(z)) 0r.
px(x) = Pz (f(x)) det
(6)
where 0f(x) is the Jacobian of ff at x. The bijection function f can be decomposed as f = f1 o...o 0z
fu-
We use a flow- RealNVP with coupling layers (Dinh et al. 2016). To provide the input to ReaINVP, we apply PCA for the dimension reduction of each extracted features. Finally, given the
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Training
Training
Training
Training (ID)
Terami
Test datases (OOD)
bounddrry
Test dalaset (00D)
Test dalasee (ID)
Test dolaset (ID)
(a) Texture OOD
(b) Semantics OOD
(c) Texture ID
(d) Semantics ID
Figure 5: Comparison of the benchmark scenarios. (a, Out-of-distribution detection scenario. The training dataset is in-distribution (ID), while test datasets are out-of-distributie (OOD). Note that texture and semantic OODs are entangled in the conventional benchmark. (c, d) Evaluation on the robustness. Unlike (a, b). it provides the ID samples at test time. An oracle in this scenario produces AUC 50.0 (cannot distinguish as OOD at all) since the given samples are from ID.
in-distribution training dataset D, the objective of the RealNVP model with the trainable parameters 0 and is to minimize the following equations.
1 N
Ltexture (D)
log pooooT))))la Esemantica (D)
log Po (55:))()))
N
By decomposing to the texture and semantic components based on probabilistic modeling, one nice side-effect is that we can adjust the contribution of these components by referring to the given OOD environment. Since we designed the feature components to be separate, we combine them with simple linear interpolation follows.
À log p (5()))z) + (1 - X) log (T
where À [0, is the control parameter (default is 0.5).
4 EXPERIMENTS
In this section, we demonstrate the effectiveness of our proposed method on various benchmark se- tups. In Section 4.1 we report the performance on the conventional OOD detection task and discuss the limitation of the previous OOD detection studies. We use the area under the curve (AUC) for the receiver operating characteristic (ROC) curve to evaluate the OOD detection performance. Then, we will discuss on how evaluate the robustness that the OOD detection method should have and show the robustness of the previous and ours models (Section
4.1 CONVENTIONAL OUT-OF-DISTRIBUTTE DISTRIBUTIO DETECTION
Setups. We evaluate out-of-distribution detection methods on the widely used OOD detection bench- mark. Here, we use SVHN (Netzer et al., 2011), CIFAR-10 10 (Krizhevsky et al. 2009) and CIFAR-
100 KKizhevsky al. 2010) as the in-distribution dataset. To simulate the OOD samples, LSUN (Yu et al. 2015) and
(Torralba et al. 2008) datasets are additionally used.
We compare our approach to the methods belonging three different OOD detection groups. 1) Methods that use class labels of the training samples. Feature based methods such as Maha (Lee et al.) 2018) and Gram (Sastry & Oore (2020) fall into this category. 2) Methods that utilize additional distribution (dataset) such as OE (Hendrycks et al. (208)) and OEC (Papadopou- los et al. 202T). 3) self-super based methods. Rotation-based (Rot) (Hendrycks et al. 2019), SSL (Mohseni et al. 2020), and CSI (Tack al. (020) in this group.
Baselines.
Results. As shown in Table I our proposed method surpasses the competitors on the conventional OOD detection task without using any extra information such class labels, other datasets, image transformation techniques that the other methods required. In detail, ours with À = 1.0 (semantic mode) achieves the best performance in all the cases with the exception of two scenarios.
Discussion. As we have discussed, it is often risky to assume that the input samples of the real- world system have single and entangled characteristic. We argue that it is more natural consider

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Using Labels
Other
Dist. OEC
Self-supervised
Ours
ID OOD
Maha
Gram
OE
Rot
SSL
CSI
0.0
0.5
1.0
C10
99.3
97.3
99.3
99.8
99.8
93.9
99.9
100.
SVHN
C100
99.0
99.9
99.8
91.1
99.8
99.9
TinyImgNet
99.3
97.3
99.5
100.
100.
LSUN
99.9
99.8
99.9
99.9
99.9
99.9
100.
100.
SVHN
99.1
99.5
98.2
99.2
97.8
99.2
99.8
86.1
99.9
99.9
C10
C100
88.2
79.0
92.9
93.8
82.3
93.8
89.2
55.7
93.6
93.5
TentImgNet
99.5
99.7
65.4
99.9
99.9
LSUN*
99.3
99.9
96.4
98.9
92.8
98.9
97.5
99.9
99.8
81.9
SVHN
98.4
97.3
82.8
95.8
95.8
80.0
99.9
100.
C100
C10
77.5
67.9
77.5
77.7
77.7
49.4
83.1
84.2
TinyIngNet
97.4
99.0
91.7
100.
100.
LSUN*
98.2
99.3
79.5
88.8
88.8
99.9
100.
100.
Table 1: Conventional OOD detection benchmark. We evaluate the detection performance by
AUC (in- vs. out-distribution detection based on confidence/score) in percent (higher is better). C10 and C100 stand for the CIFAR-10 and CIFAR-100. * indicates the high-resolution dataset.
that the data samples have multiple attributes such as texture and semantic information (Figure 5a and 5b).. For example, when the given environment requires to detect the textural discrepancy OOD, then the detection method should concentrate on the textural side alone, not the semantic counterpart, and vice versa for the semantics driven OOD case.
Unfortunately, conventional OOD detection benchmarks are developed measure the disentan- gled view of the in in-disriibution Here, we dissect the traditional benchmark to quantify the effect of each property we decompose. We first categorized the datasets into the high- and low-resolution groups by the image resolution: C10, C100, SVHN, and TinylmageNet belong to the low-resolution (L.) group while LSUN high-resolution (HR). Table shows that previous studies achieve high performance on the LR >> HR scenarios and label-based methods produce outstanding results. How- ever, in Section 4.2. | we will show that the superior performance these methods is because they abuse the texture information (e.g., detect as OOD by highly referring to the image resolution).
On the other hand, we argue that the semantic property the key component to identity the OOD of the LR + LR scenarios. For example, C10 <> C100 solely requires semantic information detect OOD since these datasets use very similar images (in terms of the texture and image resolution). This is the reason why ours with 0.0 (texture mode) is not able detect OOD at all (55.7 and 49.4 AUC). Note that other competitors also show the inferior performance, especially C10 ++ C100 case, which demonstrate that these methods have weakness in handling semantic information.
4.2 EVALUATING THE ROBUSTNESS OF THE OUT-OF- DIT-OF--DITTIBUTION DETECTION METHOD
In this section, we evaluate the robustness of the OOD detection method. The following experiments motivated by the real-world demands that the OOD detection framework focused on the semantic discrepancy should be tolerant to the minor changes in the image space (such as the texture shown in Figure(3c). and vice versa (Figure (d)). However, how can we evaluate the robustness of the OOD detection method in texture and semantic perspectives?
To quantify the robustness of the OOD method, we introduce the AUC score-based measurement. The evaluation protocol is as follow: 1) We first train the OOD detection method with the in- distribution training dataset. 2) We provide the in-distribution samples as test dataset. Here, con- trary to the conventional benchmark, AUC = 50% is the best performance. This is because 50% AUC indicates that the detection method cannot distinguish the test samples as OOD (determines as in-distribution). In Section 4.2.1 we evaluate the robustness the texture discrepancy by provid- ing the textually different (but marginally) in-distribution dataset at test time (Figure 5c). Then, in Section 4.2.2| we benchmark the robustness the semantic discrepancy (Figure 5d)

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4
Frost
noise
Haze
Motion
MNIST
K-MNIST
F-MNIST
(a) CIFAR-10 w/ corruptions
(b) MNIST variants
Figure 6: Example of the datasets used in our proposed benchmark. (a) Corrupted CIFAR-10
dataset evaluate the robustness on the texture discrepant in-distribution case (Section 4.2.
MNIST variants datasets for the semantic discrepancy scenario (Section 4.2.2..
Level
Level
3
5
ODIN
67.4
71.9
78.6
79.4
80.5
ODIN
76.0
74.5
75.3
71.0
68.0
Maha
79.3
89.8
95.9
96.2
98.2
Maha
99.0
99.1
99.8
99.8
99.9
Gram
99.7
99.9
99.9
99.9
99.9
Gram
99.0
99.9
99.9
99.9
99.9
CSI
65.1
73.3
81.1
81.8
86.9
CSI
54.6
59.8
66.1
74.1
89.2
0.0
58.1
68.8
79.7
80.3
86.5
0.0
50.1
59.9
68.1
89.0
99.9
À
0.5
58.1
62.2
72.7
77.9
82.9
À =
0.5
50.1
56.6
67.4
74.9
72.2
1.0
56.8
54.9
57.8
63.9
70.8
1.0
49.9
57.7
59.9
64.4
69.9
(a) Frost
(b) Haze
Level
2
3
5
Level
3
5
ODIN
63.1
66.0
72.4
79.9
82.0
ODIN
69.9
75.5
76.4
81.2
87.2
Maha
84.9
88.8
95.9
96.9
97.9
Maha
95.8
98.6
99.7
99.8
99.9
Gram
99.8
99.9
99.9
99.9
99.9
Gram
99.5
99.8
99.9
99.9
99.9
CSI
67.7
77.5
85.1
85.1
90.1
CSI
77.5
85.5
94.5
96.2
98.1
0.0
67.3
81.3
88.6
88.9
93.1
0.0
68.1
70.2
79.3
89.9
92.3
À
0.5
64.9
66.0
83.3
88.6
93.0
X
0.5
59.7
63.3
68.4
79.3
85.7
1.0
51.1
52.1
61.4
61.6
64.7
1.0
56.8
59.2
63.6
70.0
78.3
(c) Motion blur
(d) Shot noise
Table 2: Robustness on the texture discrepancy produced by the mild corruptions. We use CIFAR-10 as the in-distribution dataset, and corrupt this using mild distortions. In this benchmark, an OOD detection method should not determine the corrupted images as OOD (i.e., 50.0% AUC is the best score). This scenario is motivated by the real-world applications that context information the key factor in OOD but the mild corruptions are acceptable.
4.2.1 ROBUSTNESS ON TEXTURE DISCREPANCY THE IN-DISTRIBUTION
Setups. Here, we experiment on two benchmark scenarios: 1) distortion and 2) image resolution. Both cases assume that the definition of the OOD is the disparity of semantic information. That is, a mild change presented in the image space should not be considered as a signal of the OOD.
For the distortion scenario, we corrupt the CIFAR-10 dataset with frost, shot noise, haze, and motion blur distortions with diverse (but mild) corruption levels (Figure 6a). To make the resolution change case, we first perform center-crop and resize back the original resolution (32x32). We carefully adjust the cropping operation not to harm the semantic information.
Result. Table shows the AUC score of the OOD detection. Since this experiment focuses on detecting the test samples as the in-distribution, 50.0% AUC is the best score. We observe that the deep canture-bbsed methods (Maha and Gram) do not have robustness against the texture discrepancy at all. They not appropriate for real-world applications that do not treat mild corruption OOD. On the contrary, our method with À = 1.0 (semantics mode) achieves the best performance in all the scenarios because this concentrates the semantic information alone.
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Resolution 32 +
MNIST +
Method
Method
36 ())
402 (V) 44 (4)
48 (4)
K-MNIST (4)
F-MNIST (1)
ODIN
55.7
62.7
67.5
74.2
Maha
91.6
96.8
Gram
66.5
74.8
82.4
92.9
OE
97.6
99.8
CSI
81.5
86.1
89.9
93.5
SSL
99.9
100.
0.0
59.9
67.7
75.8
82.8
0.0
50.6
91.8
= 0.5
50.2
55.8
71.2
72.8
A = 0.5
100.
100.
1.0
50.1
55.0
60.9
66.0
1.0
100.
100.
Table 3: Robustness on the texture discrepancy. We use 32x32 CIFAR-10 as the ID and vary the resolution to make test datasets. Note that 50.0 is the best score since the test datasets are from the in-distribution with different image resolutions.
Table Robustness on the semantic dis- crepancy. In the K-MNIST scenario, 50.0 is the best since no textural difference ex- ists between the ID and OOD, in contrast to the F-MNIST (higher better).
In the resolution change scenario (Table 3), our method with 1.0 (semantics mode) outper- forms the others in all the settings. All the methods except ours with semantics mode are extremely sensitive the image resolution change, although no other information is modified.
4.2.2 ROBUSTNESS ON SEMANTIC DISCREPANCY OF IN-DISTRIBUTION
Setups. To evaluate the robustness that may arise from the semantic discrepancy, we use MNIST, K-MNIST, and F-MNIST datasets. K-MNIST comprises of Japanese characters while F-MNIST collection of fashion objects. In this experiment, we set both MNIST and K-MNIST as the in- distribution datasets since they are similar terms of the texture aspect (only the character labels different, shown in Figure (b). On the other hand, MNIST ++ F-MNIST is the conventional OOD scenario since they have both disparate semantics and textures.
compare method with the OOD methods that class labels or with self-supervision based approach. For MNIST -> K-MNIST scenario, our method with X = 0.0 suc- cessfully determines that K-MNIST the in-distribution (e.g. AUC is 50.6). In contrast, our model with other values (0.5 and 1.0) cannot, since these mostly depend on the semantic information to detect the OOD. This behavior also appears in the other OOD detection methods because they use entangled information. Not surprisingly, all the methods identify that given test samples are OOD in the MNIST F-MNIST case, where the texture information is the crucial component. series of experiments prove that the texture and semantic cues should be viewed as disentangled factors.
Result. In Table
CONCLUSION
In this work, we introduce novel viewpoint of the in-distribution for the practically applicable OOD detection in the real world. To do that, we separate the definition the "single-mode" in- distribution to the "texture" or "semantics" factors by following the requirements of the real-world applications. To effectively handle both aspects, we take divide-and-conquer strategy that extracts the features using the appropriate method in each factor, then combines these with the normalizing flow-based model. By doing so, our method outperforms previous models on not only our newly proposed benchmark scenarios but also the conventional OOD detection cases.
We hope that our work provides useful guidance for future OOD detection work. In the future, aim to investigate more diverse factors beyond the textures and the semantics that complicated datasets can have, such as multi-object scenes.
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Reviews:
[{"review_id": "w7BSbYxb_V", "reviewer": "Reviewer_nEb7", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "## Strength\n### 1. The paper is well organized and written, such that it is easy to follow.\n### 2. The proposed texture and semantic decomposition idea is technically sound and correct. The motivation of the idea is clearly explained. The experiment is extensive and sufficient to justify the proposed idea.\n\n##Weakness\n### 1. The paper assumes that the texture and semantic information are independent. In my understanding, this assumption is a little strong. How to make sure the assumption is valid or when the assumption is valid. This may need some justifications.\n", "summary_of_the_paper": "The paper propose to decompose the out of distribution sample detection task into two-fold: texture and semantic. Extensive discussions and experiments are presented to demonstrate the efficacy of the proposed decomposition idea.", "summary_of_the_review": "Overall the paper is good and ready for publication.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "nnVDC94YZ2", "reviewer": "Reviewer_7WrH", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "Strengths:\n- The separation of texture and semantics is reasonable\n- SoA performance \n- Easy-to-understand writing and illustration\n\nQuestions and weaknesses:\n- Is there any case in which texture is part of semantics?\n- How do we know the optimization of (2) will extract semantic information but not the texture information?\n- It looks like the method with only semantics (no texture enforced, lambda=1.0) works the best in most cases. Is it still important to enforce texture information extraction?\n- Lack of comparison to latest methods (e.g. [A], [B], [C])\n[A] Zisselman et al., Deep Residual Flow for Out of Distribution Detection, CVPR20\n[B] Lin et al., MOOD: Multi-level Out-of-distribution Detection, CVPR21\n[C] Zaeemzadeh et al., Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces, CVPR21\n", "summary_of_the_paper": "The paper proposes an OOD setting emphasizing on texture and semantics. The authors propose an OOD detection method which disentangles texture and semantics. The method achieves SoA performances.", "summary_of_the_review": "On the good side, the proposal of OOD problem with texture and semantics is reasonable and the proposed method achieves SoA performance. However it still lacks comparison with some latest methods.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "M2sn4D24imC", "reviewer": "Reviewer_sj54", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "The submission makes the case that practical applications can sometimes call purely for a detection of textural distributional shift, and at other times purely for an object-identity shift. While this certainly makes some sense, I think this perspective is a bit too narrow: all of practical OOD detection in computer vision would not fit into one of these two buckets. For example, one might be interested in the composition of scenes, where the presence of two objects that don’t usually co-occur is considered OOD. Or one might be interested in objects poses, where a car that is overturned on the road is considered OOD. This point is made in [1], where the meaning of the word “semantic” is associated with the context, thus placing every practical OOD/anomaly detection problem into first specifying context and then developing a model that captures the notion of what is semantic within that context (which would include texture, if that was the feature of interest). I believe this is a broader and more encompassing view of the point the submission is attempting to make. However, [1] did not explore beyond the context of object-identity, and this is worth exploring further.\n\n\nIn Eq. (1), I think one cannot drop the $p(x)$ term from the denominator, since we are modelling a (conditional) distribution on the variable $x$. $p(t, s)$, $p(t)$, and $p(s)$ may be dropped if one is not concerned with normalization, but I think $p(x)$ ought to remain since it is not uniform. It is also not completely clear to me that texture and semantics should be treated as independent, as in the MNIST-KMNIST experiment (regardless of their conditional independence given x), because a change in semantics ought to also entail a change in textures most often (since a novel object will most likely have a different texture associated with it). But I partially agree that sometimes we might want to ignore stylistic changes if the object stays the same. An earlier attempt at evaluating non-semantic and semantic shift exists [2].\n\n\nFrom Eq. (9) it appears that \\lambda = 0.0 corresponds to “semantic”-shift, but the experiments seem to suggest the reverse. Could you clarify?\n\n\nI’m not sure if it is notationally accurate to say that a realNVP model on top of PCA-reduced features of an image can be referenced as the conditional distribution $p(x | f(x))$. It might be more correct to say $p(f(x) | x)$ is what’s being learned.\n\n\nMethod-wise, the novel contribution is really the bit about building density models on top of PCA-reduced features from existing methods for OOD detection [3,4]. The experiments seem to suggest this can lead to fairly good performance. An ablation study is required for supporting the proposed choice of initialization for multi-SVDD.\n\n\n[1] Detecting semantic anomalies, Ahmed et al.\n\n[2] Generalized odin, Hsu et al.\n\n[3] Deep multi-sphere SVDD, Ghafoori et al.\n\n[4] Amplitude-phase recombination: rethinking robustness, Chen et al.\n\n[5] Transfer-based semantic anomaly detection, Deecke et al.\n", "summary_of_the_paper": "The submission proposes to evaluate OOD detection problems with regards to two aspects — detect distributional shift in texture, or that in object-identity. A Fourier transform is used to identify changes in texture, and a modification of SVDD is used to identify changes in object identity, by building density models on top of a PCA-reduction of the extracted features in both cases. Experiments are conducted which showcase that the two components can detect textural vs. object-identity shift while not mis-identifying one type of shift for the other.", "summary_of_the_review": "The submission is commendable in joining a set of recent papers [1,2,5] in pushing for properly benchmarking and defining OOD detection. The novel perspective in this particular submission seems to be the suggestion to disentangle texture from object-identity in a way that detecting one does not imply detection of the other, which is an interesting and thought-provoking view, albeit somewhat narrow. The idea of using density models on PCA-reduced features is demonstrated to work well. The submission scores low on clarity of notation.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "3XTjj94gidt", "reviewer": "Reviewer_q2kp", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "My biggest concerns about this paper is the correctness of assumptions, derivations and fairness of comparison.\n\n1. in section 3.1 'we assume that both variables have conditional independence given x'. Is it a valid assumption? \n2. Equation 1 is wrong. It should be p(x|t)p(x|s)p(t)p(s) / p(x)p(t, s). The priors were ignored. And I still doubt the likelihood can be decomposed wrt. texture and semantics. For example, the texture can determine whether it is a horse or zebra, grassland or sand. \n3. in section 3.2, 'p_theta(x|t) should focus on extracting texture information alone, while p_theta(x|s) solely on the semantic counterpart', the model design does not guarantee this.\n4. Figure 4 shows corrupted C10 has more frequency components at 10-20, which is not surprising. I am surprised that C10 and C100 have the same spectrum. Have you tried corrupting this C10 image by alpha-blending C10 and C100 together? Or cut C10 into pieces and rearrange the spatial orders.\n5. In table 1, what is the comparison of in-distribution detection of the proposed methods against the listed methods?\n6. In table2&3, looks like lambda=0 performs worse than method in comparison because lambda=0 is texture mode and it is testing texture discrepancy. In table 4, when testing semantics discrepancy, lambda=1 works worse because it is semantics mode. It is unfair to compare their best models with other methods, because the methods with best lambda include the prior knowledge whether OOD is wrt. texture or semantics. In table 2&3 when you choose lambda=1, you are explicitly telling the model that OOD is wrt. texture. Other methods do not have such kind of knowledge.", "summary_of_the_paper": "This paper decomposed out-of-distribution into texture and semantics and proposed a model that extracts the texture and semantic information separately and then combines them via normalizing flow-based method.", "summary_of_the_review": "I have some questions that could influence my understanding of the basic blocks. I cannot make a clear decision until seeing the answers.\n\n==========================\n\nBased on other reviewers' comments and authors' responses, I would like to lower the score to reject this paper. The false claims and equations give me an impression the authors do not understand what they are doing. I would recommend another round of revision and rebuttal for authors to build a solid story instead of rushing this work into publication.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a categorization of out-of-distribution examples by texture and semantics, and proposed a model that extracts the texture and semantic information separately before combining them via a normalizing flow-based method to obtain good results. While the categorization provides some interesting perspectives, most reviewers found the assumptions too strong, and there are some issues with the derivation. Reviewers have some positive feedbacks on the proposed algorithm for OOD, but also expressed concerns about the fair comparison with more recent baselines. The paper, in its current form, is not ready for the publication, but the authors are encouraged to improve the paper with reviewers' suggestions and resubmit.
Decision: Reject