Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
Authors: Jiyeon Han
Hwanil Choi
Yunjey Choi
Junho Kim
Jung-Woo Ha
Jaesik Choi
Venue: ICLR 2023 notable top 25% (2023)

Abstract: Evaluation metrics in image synthesis play a key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metrics are derived by comparing distributions, and thus they cannot quantify the diversity or rarity degree of each generated image. In this work, we propose a new evaluation metric, called `rarity score', to measure both image-wise uncommonness and model-wise diversified generation performance.   
We first show empirical observation that typical samples are close to each other and distinctive samples are far from each other in nearest-neighbor distances on latent spaces represented by feature extractor networks such as VGG16. We then show that one can effectively filter typical or distinctive samples with the proposed metric. We also use our metric to demonstrate that the extent to which different generative models produce rare images can be effectively compared. Further, our metric can be used to compare rarities between datasets that share the same concept such as CelebA-HQ and FFHQ. Finally, we analyze the use of metrics in different designs of feature extractors to better understand the relationship between feature spaces and resulting high-rarity images. Code will be publicly available for the research community.

Published as a conference paper at ICLR 2023
RARITY SCORE: A NEW METRIC TO EVALUATE THE UNCOMMONNESS OF SYNTHESIZED IMAGES
Jiyeon Han', Hwanil Choi', Yunjey Choi', Junho Kim², Jung- Woo Ha², Jaesik Choi
1 Kim Jacchul Graduate School of AI, KAIST
2 NAVER AI Lab
{j han, hwanil choi, jaesik.
ac.kr, kr,
yyunjey, choi, jhkim.ai, jungwoo. ha jaaeeveerrrrppcom
ABSTRACT
Evaluation metries in image synthesis play key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metries derived by comparing distributions, and thus they cannot quantify the diversity or rarity degree of each generated image. In this work, we propose new evaluation metric, called 'rarity score', measure both image-wise uncommonness and model-wise diversified generation performance. We first show empirical observation that typical samples are close to each other and distinctive samples are far from each other in nearest-neighhor distances on latent spaces represented by feature extractor networks such as VGG16. We then show that one can effectively filter typical or distinctive samples with the proposed metric. We also use our metric to demonstrate that the extent to which different generative models produce rare images can be effectively compared. Further, our metric can be used to compare rarities between datasets that share the same con- cept such as CelebA-HQ and FFHQ Finally, we analyze the use of metrics in dif- ferent designs of feature extractors to better understand the relationship between feature spaces and resulting high-rarity images. Code will be publicly available for the research community!
INTRODUCTION
Generative models have attracted a lot of attention for recent years. Generative Adversarial Net- works (GANs) (Goodfellow et al. 20144) have achieved significant advancement over the past several years, enabling many computer vision tasks such as image manipulation (Bau et al. 2019 Jahanian
2020 Shen et al. 2020 Härkönen et al. 2020, Kim al. 2021). domain translation (Isola et al.
al.
2017 Zhu et al. 2017 Choi et al.] 2018 Kim et al. 2019 2020 Choi et al. 2020). and image
or video generation Tulyakov et al. 201 8: Karras et al. 2019 2020b a 202 Skorokhoodov et al.]
2022 Tian et al., 2021] Kim et al. 2022 Kim & Ha 2022 Lee et al. [022]] Yu et al.. 2022b). The emergence of diffusion models accelcrates the advancements of the generative models especially
in the field of text-to-image modeling (Ramesh et al. 2022 Saharia al. 2022 Yu et al. 2022a
Rombach et al. 2022)).
To quantify the performance of generative models, various metrics have been proposed. As stan- dard evaluation metrics, inception score (IS) (Salimans et al.) 2016), kernel inception distance (KID) (Binkowski et al. al.] (0118), and Frechét inception distance (FID) (Heusel et al. 2017) preva- lent for evaluating the quality of images synthesized by generative models. These metrics evaluate the discrepancy between generated and real image sets on the feature space characterized by gener- ative model with respect to diversity and fidelity. The fidelity represents the quality of the generated image, and the diversity indicates the variety among the generated images that the generator creates without mode collapse similar to the distribution of the training datasets. To improve the fidelity and diversity, it is required that the distribution of generated images is similar to the real image distribution (Kynkäänniemi et al. 2019 Naeem et al. 2020)
available at http::/:///tttubccocec htt

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Smallest NND
Middle NND
Largest NND
FFTO
AFIQ Wild
MedF acos
Figure 1: Real samples with the smallest nearest neighborhood distances (NNDs), middle NNDs, and the largest NNDs, respectively. For 'Middle NND' column, the images are randomly selected among the middle-ranked 200 images.
On the other hand, reproducing capability of the low density area is often overlooked. we consider the distribution of training data, clear that the rare samples in the low density areas are poorly represented and thus likely to be difficult for the generator to reproduce. Examples of rare samples from public datasets include people with various accessories FFHQ (Karras et al., 2019), white animals in AFHQ (Choi et al. 2020), and uncommon statues in MetFaces (Karras et al. (020a).
Assessing the rarity important not only because it is related assessing the reproducing capa- bility of the generative models, but also because it is related to selecting generated images. Recent commercialization of sext-oo-mmaee models such as DALL-E (Ramesh al.) 2022) and Stable Diffusion models (Rombach et al., 2022) enhance the necessity of metrics for uncommonness and creativity. these creative Al application scenarios, a metric that measures each synthesized image (instance-wise metric) is essential for users and consumers to select images among the ones provided through API, rather than model-wise metrics such as FID (Heusel et al., 2017) and LPIPS (Zhang et al. 2018). Unfortunately, instance-wis metric exists for measuring the creative and uncom- mon degree of each image despite its practicality and necessity.
In this paper, propose novel generative model evaluation metric that represent the gener- ative capabilities of rare samples in generative models as scores (a.k.a. rarity score). Our metric contributes to classifying rare images and typical images similar those frequently observed in training datasets. The proposed rarity metric highlights the open problem of sparse generation of rare samples from generative models. Additionally, we have conducted comparative experiments on which of the previous state-of-the ff-thhe-n art models produce more rare samples with preserving quality performances. Our contributions can be summarized as follows:
We propose the first metric to quantify the rarity of individual generation which existing metrics cannot provide. Using the proposed metric, generations with the desired degree of rarity can be sampled.
We show that the proposed metric can be used compare the capability of generative models to generate rare samples. The proposed metric can further be used to compare which dataset contains more rare samples among the datasets that share the same concept such as CelebA-HQ and FFHQ datasets.
We show the proposed metric can be applied on top of the various feature spaces with different viewpoints of rarity, by analyzing the sampled generations.
2 PRELIMINARIES
Precision and Recall Precision and recall commonly used performance metrics in many areas including classification tasks or natural language processing. In specific, to quantify the perfor- mance of the generative models, precision measures the fraction of the fake distribution that can
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be generated from the real distribution. On the other hand, recall measures the fraction of the true distribution which can be reproduced by the fake distribution. Practically, k-nearest neighbor (k- NN) based method has been proposed to estimate the real and fake manifolds (Kynkäänniemi al. 2019). For real samples X, ~ Pr and fake samples Xg ~ Pg. we first embed them in the high- dimensional feature space using pretrained DNNs such as VGG16 (andAndrew Zisserman 2015) or the image encoder of CLIP (Radford et al. (2021) to get sets of feature vectors D, and $ re- spectively, The real and fake manilolds estimated by the sets of k-NN spheres of each sample as follows.
manifold; ((a ( = U 6:
Bx Φ), (x 2 = {old(o.. = NN.( Φ)
(1)
Here, N (d,, 4) represents the distance between de and its k-th nearest neighbor in 4. BR +) is the k-NN sphere of Φ; with the radius of N (dx +) defined as a set of all á whose distance to Φ is smaller than or equal to N (o,, $). For the distance metric we use L2 distance for the rest of the paper. Then, precision and recall are respectively defined as
precision d 2g)
1
Σ
I1(Ø, E manifold; (*+)),
dy 6yE
recall p,, D2)
I(d; E manifoll; (4))) (4x))
(3)
where I is indicator function. Precision often maps with the fidelity and recall maps to the diversity of the generations. However, it is nontrivial to apply both precision and recall to an individual generation they designed to measure the sets of samples. To quantify the image quality of an individual generation, realism score has been proposed. Realism score measures the maximum of the inverse relative distance of fake sample in real k-NN sphere.
NN $.)
realism score
max 0.E
K))
Realism score is high when the distance between the given fake sample and a real sample is relatively small compared to the radius of the k-NN sphere of the real sample. However, the realism score is not suitable to find rare samples as realism score can be large even if the radius of the k-NN sphere small when the distance between fake sample and a real sample is also very small.
Density and Coverage While precision and recall commonly used, there are still several draw- backs that they are vulnerable to the outliers and computationally inefficient. To overcome the draw- backs, density and coverage have been proposed (Naeem et al. 2022). While precision only counts whether the fake sample is inside the real manifold or not, density counts the number of the real k-NN spheres that contains the fake sample.
density dr. Lgg
Σ
Σ
1(, E BB(dr, 2,))
(5)
6. d:E
If fake sample is included in the multiple real k-NN spheres, it more certain that the fake sample is inside the real manifold. Thus, density can be more robust to the outliers compared to the precision. On the other hand, similar to recall, coverage is relevant to the diversity of the generations. Coverage computes the number of real samples containing at least one fake sample in its k:-N sphere.
1
coverage Φ,, +g)
Σ
I(30, &g. s.t., Ø5 E Bxld,, D.))
(6)
fake sample is sparsely located, the fake manifold can be exaggerated and the recall can be overestimated. As real manifold is known to have less outliers than the fake manifold, coverage can prevent such overestimation. Furthermore, coverage is more computationally efficient compared to the recall as coverage does not require the k-NN computations of fake samples. However, density and coverage are also not suitable to be applied on individual generations as they work on set of generations.
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Red
di
(a) Case 1: Single sphere
(b) Case 2: Multiple spheres
(c) Case 3: Out of real manifold
Figure 2: Real and fake images and their embeddings in the feature space when calculating the rarity score. The transparent circle represents the k-NN sphere of the corresponding real sample at the center. (a) When 91 is only in k-NN sphere of rı. 91 is scored by the radius of r1 (d1). (b) When 92 both k-NN spheres of r2 and r3, f2 is scored by the smallest radius (d2). Since II has the higher score than 92. fi looks more unique than 92. (c) If sample is out of real manifold as gs. we cannot guarantee the fidelity of the image and thus exclude it from the scoring procedure.
Truncation Trick The truncation trick is widely used in generative models restrict latent sam-
pling space to maintain the image quality (Kingma & Dhariwal 2018 Brock et al.] 2019 Karras
al. 2019). Typically, in StyleGAN based models (Karras al. T 2019] 2020b a). the truncation trick is applied in the warped latent space W from the input latent space 2 with f 2 W as follows.
w'
( (w w), = [f
When d 1, it is the same not using the truncation trick. As t decreases, the latent code shifts toward the mean latent code. When 0, w' becomes w, which the mean latent code in W. Truncation trick is known to increase the fidelity at the expense of lowering the diversity of the generations.
3 RARITY SCORE
To evaluate the rarity of both individual generated instance and generative model performance, propose novel metric called rarity score. Following the idea of Kynkaänniemi et al. (2019) and Naeem et al. 2020). we use k-NN to represent the manifolds of real and generated samples as in Figure
3.1 INSTANCE- WISE RARITY SCORE
We hypothesize that ordinary samples would be closer to each other whereas unique and rare samples would be sparsely located in the feature space. Figure [ shows the real samples with both the smallest nearest neighbor distances (NNDs) and the largest NNDs from the training dataset. For all datasets, samples with the smallest NNDs show representative and typical images. On the contrary, the samples with the largest NNDs have strong individuality and are significantly different from the typical images with the smallest NNDs. From this intuition, we propose the rarity score:
Raritylog 2.)
min r,s.t. (B.
NNr(Or, $F).
(8)
Equation 8 measures the rarity score of the given fake sample as the minimum radius of k-NN sphere of the real sample that contains the given fake sample. Equation (8) can be viewed dual-way way k-NN, if we see the constraint dy E (a(or, 2,) as a reverse k-NN (RkNN) (Korn & Muthukrishnan 2000 Tao et al. 2004). RkNN is known which the given query is at least the k-th most influential. Thus we are filtering real samples to those that can be influential to the given fake sample. Equation X then takes I-NN to the RkNN set.
problem to find the set of samples to
For Case 1 in Figure 2 91 is in the k-NN sphere of rı, thus the rarity score 91 is the radius of the K-NN sphere of r1, which is dy. Case in Figure 2 shows that even though Ø2 is in intersection
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Low
Middio
High
Ruzey Sooee
Realiom
Scom
Trunestoo
THiS
V 0.5
Figure 3: Images generated by StyleGAN2-FFHO, sorted by the various methods. For the rarity score and the realism score, 'Low' column presents the generations with the top 5 lowest scores, 'High' column presents the generations with the top highest scores, and 'Middle' column presents the random generations in the middle of the top 5th and the bottom 5th. For the truncation trick, 'Low', 'Middle', 'High' column show random generations with the truncation parameters &b 0.5, 1.0, 1.5, respectively.
200
140
145
189
300
The (M
310 1550
1.0 Tranndion punnmuber
Buslyy Sours
(a)
(b)
Figure 4: (a) RS-1 scores (blue), Realism scores (red), and FIDs (green) according to the truncation parameter for StyleGAN2-FFHH (b) Histograms rarity score according to the truncation pa- rameter th. The images next to the histogram are the generations for the corresponding s/.
of k-NN spheres of r2 and r3, 92 is scored by radius of r2 (d2). Since, in addition, II looks more distinctive compared to 92. it has higher rarity score. As samples outside of the real manifold cannot guarantee their high fidelity, our metric does not consider the generations not contained the real manifold manifold; ( For example, the generation ga in F () is an artifact and it is out of the real manifold. Thus the rarity score for Os is undefined. This ensures the fidelity of the selected images without any additional fidelity metrics. The synthesized examples out of the real manifold are presented in Appendix E Throughout this paper, we use = for the experiments, which is empirically chosen with the supports in Appendix
3.2 MODEL-WISE RARITY SCORE
Model-wise Measurement As rarity score is defined an instance-wiss measurement, there can be many ways to aggregate the rarity scores of set of fake samples compare between generative models. In this paper, we suggest one way of model-wise measurement, RS-p, based on the rarity score.
RS-p(O)
Σ Rarity(0g), 2, (6g @ 0,E0,
p/100}, (9)
where 2 denotes the set of fake samples generated by a model O We omit 4, in Rarity as it is fixed. Basically, RS-p is the mean rarity score of the fake samples with the top p% rarity scores. RS-p is designed to compare the rarity of the most rare generations.
Relationship with Existing Metrics Here, we mainly investigate the relationship between our rarity and two existing methods that most relevant ours: the realism score (Kynkäänniemi et al. 2019) and the truncation trick (Brock et al., 2019). The realism score is a metric which measures the fidelity of an individual generation. While the realism score takes into account the
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Bottom 8
Middle
Top 8
FFHQ
StyleGAN2
LSUN-Car
StyleGAN2
AFHQ-WiiL Hil-
-ADA
StyleGAN3
MctFaces
CclebA-HQ
NVAE
Figure 5: Bottom 8, Top 8, and Middle 8 generated images in terms of the proposed rarity score five datasets and four generation models.
radius of the real k-NN sphere, the effect of the radius is degraded by the effect of the distance between the real and the fake sample. Realism score can be high for the samples in the high density region which is assumed to be typical and thus not suitable to measure rarity. In Figure 3 while images with the high realism scores show high fidelity, the images with the high rarity scores show more extraordinary characteristics in the images. We further compare our metric with the inverse realism score by conducting a user study. The results are summarized in Appendix 1.3
Truncation trick has been proposed to maintain high fidelity of generations by filtering out the out- skirt latent codes far from the mean latent code. This method increase the fidelity of the images, but it can compromise its versatility. If we use & 2 1 to shift the latent code in the opposite direc- tion from the mean latent code, we might get more diverse images. In this case, however, we cannot guarantee the fidelity of images as shown in Figure
As it is known that the truncation trick shows diversity-fidellis trade-off, it will be convenient to compare with other metrics based on the truncation parameter changes. When truncation parameter increases, one can presume that the diversity will increased and likely the rarity of generations as in Figure This is well shown in Figure/4a| where rarity score increases as the truncation parameter increases. However, is hard to be captured with FID or realism score, as (1) FID penalizes when the distribution is far from that of real as in the cases of focused on the fidelity, On the other hand, as rarity score does not measure the fidelity directly, will be helpful rarity score along with the fidelity metrie, for selecting images models.
1.0 and (2) realism score is more
EXPERIMENTAL RESULTS
In this section, we present the experimental results of the proposed metric for various standard GANs and datasets. We use & 3 for the manifold approximation throughout the experiments. We use 30k real images to approximate the real manifold and calculate the rarity of 10k fake images. We VGG16 feature extractor for all experiments except for Section 4.4

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Aligned
Not aligned
Similar
Total
84
5
10
Group 10 vs
88
Group
13
Figure 6: Alignment of human evaluation and rarity score.
4.1 RARITY OF INDIVIDUAL GENERATION
Figure 5 shows synthesized images with top 8 highest, top lowest, and middle rarity scores. As expected, the generated images with the lowest rarity scores are the ones that typical in the training dataset. On the other hand, the generations with the highest rarity scores are far from typical images and have various characteristics and uniqueness that rare in the training dataset. For FFHQ dataset, for example, we can see clear and formal human face images in the 'Bottom 8' column whereas we can see elaborated face images such as face with colorful makeup fancy hat in the 'Top 8' column. For AFHQ-Wild dataset, bottom images mostly show the faces of typical tigers while top images are mostly female lions with light-colored fur, which are far from the typical lion. Further, MetFaces dataset shows oil portraits in bottom images while top images show statues or flat paintings. However, for the datasets which are uncurated and not aligned such as LSUN datasets, the top images are not clear as that of the aligned datasets because the training dataset contains lot of noisy images.
A User Study Rarity Score We have conducted score aligns with the human perception of uncommonness. 61 participants answered our survey. In this survey, we target on StyleGAN2-FFHQ model. The results of user study are summarized in Blg the details of the experimental setting is described in Appendix [
study whether proposed rarity
In total, 84% of answers were aligned with the rarity score, i.e., people interpret images with the lower rarity scores as more common images. For more challenging comparison between Group 10 (the highest rarity group) and Group 5 (middle rarity group), the alignment lesser than that of Group 10 and Group (the lowest rarity group) and the portion of answer "Both sets are sim- ilar" increases. This suggests that the proposed metric can distinguish the degree of difference in commonness.
4.2 RARITY OF GENERATIVE MODELS
Not only the proposed metric can be applied on individual generation, it can be used to compare generative models on the same dataset which shares the real manifold. While there exist diversity metrics the generative models such LPIPS (Zhang et al. 2018), our method is different in that focuses on the ability to generate unique and rare generations while the existing metrics focus on diversity between generations.
An Empirical Comparison between Generative Models We further compare the generative models using the rarity score. Since our main focus is on the rare samples, we compare models by the RS-p score which is the mean rarity score of the top p% samples among 10000 samples. The left graph Figure 7 shows the RS-p scores of the various models as p changes from 0.1 1.0. The images in the right column of Figure 7 are the top 10 images with the highest scores, which corresponds to compared with the real images by StyleGAN2 are colorful and have diverse accessories, such as crown, mic, and hair band. On the other hand, the images from UnetGAN seem to have relatively less diversity. We interpret that UnetGAN generates images more conservatively as its RS-p scores are the lowest among the models for all 0.1 < p < 1.0. We find that the OOM ratio does not affect much to the RS-p score when
0.1. Under p 0.1, StyleGAN2 shows the highest RS-p score even when
see in the generations on the right. The images generated

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RS snnro for top pl sumples
45
47.24
OZ
$1.0
Real
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$6.54
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01 07 05. 01 17., D6 07 B4 0b 1D
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Figure 7: RS-p score of the top p% samples (left) and the top 10 images with the highest rarity scores (right). The number above each image represents the rarity of the corresponding image. We investigate the mean scores of top p% samples for 5 models trained on FFHQ dataset; StyleGAN (stg), StyleGAN2 (stg2), StyleGAN3 (stg3), VQGAN (vggan), and UnetGAN (unet).
0.08
6.10
CHRAA-HQ
2,30
0.12
APHQ-Cat LSUN-Cat
FFHQ
0.04
0.0
0002
0.00
.0
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30
10
30
0.00
10
20
30
10
50
Rarity score
Rarity acotre
FFHQ
ColebA-HQ
LSUN-Car
AFHQ-Cat
Figure 8: The density of rarity score for 10000 images sampled from datasets sharing the same concept (top) and images with the high rarity scores for each dataset (bottom). Rarity score is measured on the union of real manifolds for pair of datasets.
VGG is the feature extractor. We further observe that high RS-p score does not always imply low fidelity, which is further discussed in Appendix N
4.3 COMPARISONS BETWEEN SIMILAR DATASETS
We also propose a method to compare rarity between two different datasets which share the same concept. such CelebA-HQ and FFHQ for human faces. Rarity scores of each dataset calcu- lated on the union of two real datasets, + Φr, U Dr.. For example, when we calculate the scores of human face datasets such CelebA-HQ and FFHQ, the real manifold manifold, (Φ,) is manifoldg PCCelebA A-HQ HQ U PPFHQ). Figure g shows comparisons on normalized rarity score for sets of datasets that have the same concept. In the case of human face dataset, the density of CelebA-HQ is skewed left while the density of FFHQ is widely distributed to the right. It can be interpreted as FFHQ has more uncommon images than CelebA-HQ. In other words, there are less uncommon images in CelebA-HO This makes sense because CelebA-HQ is collected among the celebrities while FFHQ is collected among the public with considerable variation in terms of age, ethnicity, and image background. The images in Figure R are top scoring images in each dataset. Since the maximum rarity score FFHQ greater than that of Cel the top images of FFHQ are relatively more uncommon. Similarly, LSUN-Cat and AFHQ-Cat are both datasets on cat images where LSUN-CA uncurated cat images and AFHQ-Cat is curated with close-up cat confirm that LSUN-Cat is more diverse than AFHQ-CA Cat the rarity
faces only. In Figure 8 score distribution of AFHQ-CA is more on the left and concentrated compared to the distribution of AFHQ-Cat.
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VGG16
DINO
CLIP
High
StyleGAN2 generations of FFHQ with the highest rarity scores and the lowest rarity scores for various feature extractors. For the low score images, however, as CLIP is one of the self- supervised models, it captures semantics rather than structures or patterns unlike the other classifiers.
Figure 9:
4.4 STUDY ON FEATURE EXTRACTORS
Since the proposed metric operates in the feature space, the perspective of the rarity may vary de- pending on which feature extractor is used. Figure@presents images with high and low rarity scores for diverse feature extractors to analyze the effects of the extractors. As classifier trained on the Imagenet dataset, VGG16 focuses more on the objects and the patterns. There are many images with colorful face painting among the high score samples with VGG16. On the other hand, ViT models such as DINO (Caron et al. 2021> are known to focus more on the structure compared to the CNN-based models (Park & Kim 2022). This can be seen from the second column of Figure 9 The DINO-based high score images have various face angles while the low score images are mostly front-facing. In the third column of Figure the baby faces appear in the low score images, unlike the other two models which have formal adult images in the low score. This can be explained from the previous study that the CLIP model (Radford et al. (021), which is a self-supervised image-text multi-modal model, can better capture semantics compared to the classifiers. We conjecture that the model may consider the baby faces more similar between them than between the other images because baby faces are less distinctive and share a lot common properties compared to the adult faces. For the user preference, we present a user study on the feature extractors for FFHQ dataset and found that VGG16 is the most preferred feature extractor for FFHQ dataset. The detailed ex- perimental setting and results are described in Appendix [ For the noise robustness, on the other hand, found CLIP is more robust to the Gaussian noise or Gaussian blur compared to VGG16 for various noise levels. The details are described in Appendix[]
5 CONCLUSION
In this paper, we propose new evaluation metric called rarity score to measure how rare syn- thesized image is based on the real data distribution. Each generated image is scored by estimating the density around the target image on the real manifold. Our rarity score enables several following tasks that existing metrics, such as FID and Precision&RReall cannot do. First, the proposed metric can evaluate the rarity or diversity of an individual image rather than a set of images. Second, the proposed metric can be applied to compare between generative models or datasets that share the same concept in terms of the density of the rare samples. In addition, we provide studies on fea- ture extractors that the viewpoint of commonness for each feature extractor varies depending on its structure (e.g. fully CNN, ViT) or how it was learned (e.g. fully supervised, self-supervised). The main limitation of our method is that it relies the training dataset. For example, for the LSUN datasets which have non-aligned images, noise images can have high rarity scores, which would not be what users expect to have strong characteristies for the target category. Further, it might reflect the bias that the training dataset may contain. However, this can be useful for studying fairness and bias in the datasets or the generative models.
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ETHICS STATEMENT
It is challenging to quantify the creativity and is not exceptional for AI's creativity. Rarity score that we propose can be regarded as a step toward quantifying creativity even if focuses uncommon- ness and uniqueness based on distribution distance. To achieve better creativity quantification, it is essential make a consensus among more multidisciplinary experts including artists, philosophers, social scientists as well as Al researchers.
Since our study is to propose metric for evaluating image synthesis and manipulation models, is inevitable to perform user study to validate the efficacy and robustness of the proposed Rarity Score. Considering that our user study to simply compare the diversity of images generated by each model and rarely give participants stress, we did not apply IRB review process. However, acknowledge Ethical review board and agree their comments that this decision needs to be approved by IRB and it is necessary to make more concrete guideline of IRB process in machine learning and data science research community.
REPRODUCIBILITY STATEMENT
We will release our code and the implementation details to be publicly available for reproducibility. We will also include the url links to access various feature extractors.
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A
RANK CORRELATIONS
StyCCANN FFRQ
SEseCAAN LS:N Car
SOGAGANG LSUN Cat
6,446
6.F.A
6.16
(a) StyleGAN2 FFHQ
(h) StyleGAN2 LSUN-Car
(c) StyleGAN2 LSUN-Cat
StraGAN Houe Hooe
SyLGANDADA KeeRass
SyACANNAADA AFRONVI
Now Purk Cuer
5"
(d) StyleGAN2 LSUN-Horse
(c) STG2-ADA MctFaces
(f) STG2-ADA AFHQ Wild
Figure 10: Rank correlations for the various models. STG2 stands for StyleGAN2.
B DISTRIBUTIONS ACCORDING TO THE TRUNCATION PARAMETER
(18)
Thene (TA)
(94)
Figure 11: Histograms of the rarity score for 10000 generations varying the trunctaion parameter vb
from 1.0 to 0.2. As expected, the distribution moves to the left as vi gets smaller (less diverse).
C COMPARISONS BETWEEN MODELS
CD
1657
1356
GB
SHACCAND
Figure 12: Top 15 samples of the highest rarity scores for the models trained on FFHQ dataset.
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D REAL SAMPLES ORDERED BY NND AND FAKE SAMPLES ORDERED BY RARITY SCORE
Lowest
Highest
Real Samples (NND)
Fake Samples (Rarity Score)
Figure 13: Real samples with the lowest/highest NND from FFHQ dataset (top) and fake samples generated from StyleGAN2 with the lowest/highest rarity score (bottom).
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Lowest
Highest
Real Samples (NND)
Fake Samples (Rarity Score)
Figure 14: Real samples with the lowest/highest NND from LSUN-CA -ar dataset (top) and fake sam- ples generated from StyleGAN2 with the lowest/highest rarity score (bottom).
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Lowest
Highest
Real Samples (NND)
LAUITHAT IM
LAD I'M
LAD THAT. I'M CLADITHAT TM
Fake Sample es (Rarity Score)
Figure 15: Real samples with the lowest/highest NND from LSUN-Cat dataset (top) and fake sam- ples generated from StyleGAN2 with the lowest/highest rarity score (bottom).
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Lowest
Highest
Real Samples (NND)
Fake Samaee ies Rkentty Score)
Figure 16: Real samples with the lowest/highest NND from LSUN-Horse dataset (top) and fake samples generated from StyleGAN2 with the lowest/highest rarity score (bottom).
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Lowest
Highest
Real Samples (NND)
Fake Samples (Rarity Score)
Figure 17: Real samples with the lowest/highest NND from MetFaces dataset (top) and fake samples generated from StyleGAN2-ADA with the lowest/highest rarity score (bottom).
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Lowest
Highest
Real Samples (NND)
Fake Sample (Rarity Soore)
Figure 18: Real samples with the lowest/highest NND from AFHQ-Wild dataset (top) and fake samples generated from StyleGAN2-ADA with the lowest/highest rarity score (bottom).
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E EXAMPLES OF OUT-OF-MANIFOLD SAMPLES
FFHQ
LSUN-Car
StyleGANZ
LSUN-Cat
LSUN-
Metfaces
StyleGANZ.ADA
AFHQ-WIO wild
FFHQ
StyleGAN3
500001 10009
MetFaoes
Figure 19: Out-of-manifold samples for various models.
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RESULTS FOR THE VARIOUS FEATURE EXTRACTORS F
VGG16
DINO
CLJ
High
StyleGAN FFHO
Low
High
StyleGGA AA-- LSUN- Car
Low
High
MARE
Low
StyleGAN2- AA---DD--MMM MesPPoost
High
LOW
StyleGANZ- SHMLCANN-ADA-RRER ADA-ARR---III
High
Low
Figure 20: Fake samples with the lowest/highest rarity scores based on various feature extractors.
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G
SELECTION OF k
Back Coneeation
2,44
Mour Tunns Carvaaa
F4
10
NEM.
Figure 21: Rank correlations between 1 < k: < 10 for the rarity scores. Left Rank correlation matrix between S k: 10 and S k/ < 10 for the fake samples in { E Mi where M, stands for manifold, (x)) with i as the k-NN parameter. Center Mean rank correlation for all ks. Each plot represents the mean rank correlation for the fake samples {0g E Mr}. Right : Portion of out of manifold (OOM) samples.
StyleGAND
StyleGAN
SDY:GAN2
Rabuo Shee
Filze Megort
(a) StyleGAN2 FFHQ
(b) StyleGAN2 FFHQ
(c) StyleGAN2-ADA FFHQ
VOCAN
UnetSAN
Raloe Nogatio Rare
FAlOe PICKEN
35
(d) VQGAN FFHQ
(e) UnetGAN FFHQ
Figure 22: False Negative Rate (FNR) and False Positive (FP) graphs for the various models trained on FFHQ dataset. FNR refers to the ratio of the number of samples that are not excluded by k-NN among the true in-distrubtion samples but excluded by k-NN. For the true --i-t--iiitiitttionn samples, manually labeled 1000 generations into normals and artifacts.
samples, i.e., artifacts. FP refers to the number of samples that are
Choice of l: We show how for nearest neighbors affects the proposed metric in terms of the Spearman rank correlation. Figure 21 shows the rank correlations among 1 k 10. The left heatmap shows the rank correlation matrix for the samples included in Mi where M, indicates manifold, (.). i.e., the real manifold built with i-NN spheres. The second plot in Figure 21 shows the average rank correlation between k and 1 < k < 10. Each color indicates the target generations in M, Please note that M, M, for S and the rank correlation changes dramatically for the cases in M, M,. For example, for My. the mean rank correlations have high values 2 0.8 for all 3. While there exists slight drop the mean rank correlation as increases, due to the change in the out-of -un-ff--m (OOM) samples, we can see that the mean rank correlation is consistent for i. In other words, the rank the proposed metric does not change much for different choices of k: and can say the proposed metric is robust to the choice of k. However, if we decrease k. the number of fake samples out of the real manifold increases as we can see in the graph on the right in Figure 21 Considering that k 2 3 keeps OOM samples under 30%, we set & = 3 for the rest of this paper. Further analyses of false-positive versus false- lllleenngativ--rate in Figure 22 support the choice
of l: =
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H
RESULTS FOR NON-GAN-BASE MODELS
Bollom
Top
NVAR
Odlci 122
LSUN
ADM
LSUN Foose
Figure 23: The samples with the lowest rarity scores and the highest rarity scores from NVAEs (Nouveau VAEs) trained on FFHQ and CelebA-HQ, and ADMs (Ablated Diffusion Models) trained on LSUN-CSE and LSUN--HoSS respectively.
USER STUDY
Aligned
Not aligned
Similar
Total
CLIP
28.90
Group 10
62.0%
9.2%
VGG16
DINO
Group
79
(b) Preference between various backbone models
(a) Alignment human evaluation and rarity score
Aligned
Not aligned
Rarity vs Realism
StyleGAN2 vs UnetGAN
68
31
(c) Comparisons between rarity score versus realism score and between Style- GAN2 versus UnetGAN trained FFHQ dataset. For rarity realism score, 'Aligned' represents that rarity is preferred compared real- ism score. For StyleGAN2 versus UnetGAN, 'Aligned' represents that Style- GAN2 is preferred compared to UnetGAN.
Figure 24: The summary of the user study.
We have conducted user study whether our proposed rarity score aligns with the human perception of commonness and which backbone model is preferred. 61 participants answered our survey. In this survey, target on StyleGAN-FFHQ model.
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1.1 HUMAN EVALUATION FOR THE COMMMONNESS
To investigate whether the proposed rarity score aligns with the human perception of common- ness/uncommonnes, asked the participants choose more typical and common images between two sets of images with different levels of rarity scores. Each set contains 9 images. The partici- pants can answer either "A" "B", or "Similar". We divided the generated samples into 10 groups according to its rarity score. Group I has the lowest rarity scores and Group 10 has the highest rarity scores. Each participants are asked to answer 10 sets of choices, sets comparing Group 10 and Group and 5 sets comparing Group 10 and Group 5. The order of sets are shuffled and the location of Group 10 (left or right) is also randomly shuffled.
The results are summarized in Figure 24a In total, 84% of answers were aligned with the rarity score, i.e., human perception of commonness matches with the low rarity scores. For more chal- lenging comparison between Group 10 and Group 5, the alignment is lesser than that of Group 10 and Group I and the portion of answer "Similar" increases. This suggests that the proposed metric can distinguish the degree of difference in commonness.
1.2 PREFERENCE ON BACKBONE
We further asked to the participants which backbone is preferred. For this survey, each participant given 3 sets of images with different backbones; VGG16, DINO, and CLIP. Each contains images and the order of backbone models is shuffled each time. Each participant answers 5 times for this type questions. As a result, VGG16 is the most preferred with 62% of times. CLIP was the second most preferred backbone, but the gap between VGGI6 and CLIP was significant.
1.3 COMPARISON WITH NEGATIVE REALISM SCORE
We additionally conduct a user study on which metric is preferred for measuring uncommonness between rarity score and realism score. We give 10 sets of images, where each contains top 9 and bottom 9 images for each of the rarity score and realism score. The location (left or right) between rarity score and realism score is randomly selected for each set. For a fairer comparison, we excluded out-of man-ffll samples from the samples of the top/bottom realism scores. We ask each participant that which set of images represents rare/common images best. 30 participants answered this survey. The result is summarized in Figure 24c In total, 84% of answers choose the image set from rarity score compared to the image set realism score. This supports that rarity score better suitable for distinguishing and measuring uncommonness realism score, while realism score useful for measuring fidelity of individual generation.
1.4 ALIGNMENT WITH RS-P
We further ask participants which model is preferred in terms of rarity between GAN models trained on FFHQ. We choose two GAN models, StyleGAN2 and UnetGAN for the comparison, which have the highest RS-p score and the lowest RS-p score, respectively. We show 10 sets of images, where each set contains top 9 images with the rarity score for each of the StyleGAN2 and UnetGAN. The result is summarized in Figure 24c 'Aligned' represents that StyleGAN2 chosen to have rarer images, which is aligned with RS-p score as StyleGAN2 has higher RS-p score compared to UnetGAN. As a result, 68% answers were aligned with RS-p score. While it difficult to clearly specify the order of rarity from the generations in Figure 7 the result of user study suggests that RS-p score can guide to choose model with more rare generations.
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J
NOISE ROBUSTNESS OF RARITY SCORE
Nankilan (V0G
Spannas (VGAI
68
Koeablltann (T)PP)
Rank Correlation
Nowe
Nake
Notar
Bhar
Bir 3
lite 5
Figure 25: Kendalltau and Spearman's rank correlations between the rarity scores of corrupted images and clean images, according to the noise levels. Noise 0.1, 0.3, 0.5 stand for the standard deviations of Gaussian noise, respectively, and Blur 1, 3, stand for the radii of Gaussian blur, respectively. For VGGI6, it is vulnerable to the Gaussian noise, the rank correlation rapidly decreases and becomes O at standard deviation 0.5. This can be improved when we use backbone more robust to the noise. As an example, CLIP shows better rank correlations compared to VGG16 for every noise level. Both backbones are more robust to the Gaussian blur compared to the Gaussian noise.
K RS-P OF FFHQ MODELS WITH VARIOUS BACKBONES
CUP
5.0
5.4
(80
196
Figure 26: RS-p of FFHQ models and samples with the top rarity scores. The set of samples for each model is marked with color of the bounding box same as the legend color.
We compute RS-p scores of various FFHQ models by adapting the backbone to VGGI6, DINO and CLIP. Figure 26 shows the results. As different backbones have different viewpoints of rarity, the order of RS-p also depends on the backbone. For VGG16, StyleGAN2 shows the best RS-p score for its colorful decorations the generations. On the other hand, for DINO, VQGAN shows the highest RS-p score which have some artifacts on the corner of the image. This is because for DINO, the image with objects other than a human face is regarded as rare. For CLIP, VQGAN again shows the best RS-p score. However, for this time, boys with curly hair have the highest scores. It turns out that, a real image of a magazine cover of a teenage star has the highest nearest neighbor distance and the generations similar to this image get high rarity scores for CLIP backbone.
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L
APPLICATION ON A TEXT-TO-LMAGE MODEL
02
24
64
24
Prompt (sorted by the occurrence
62
the small dog has tiny black nese.
Bottom
24
an image of dog sitting a sirting
dog looking away from
the wind.
grey dog with black collar sits outside the sun. close up of dog near bowl
83
No.
Prompt (sorted by the occurrence in top 10)
brown dog standing
toilltt bathroom,
Top 10
an cooking at kitchen counter near a baby and a dog.
1.5
05
67
67
84
48
man in the kitchen standing with his dog.
12 dog standing next toilet playing with tissue
three people
tailking next a dag.
Figure 27: The bottom 10 and top 10 generations from Latent Diffusion Model (LDM) and the prompts from MS COCO dataset. The number above each image represents the corresponding prompt number generate the image.
We compute the rarity scores on a LDM (Latent Diffusion Model), recently released text-to-image model. For the experiments, we use cable-diffusion--1133 from the official git repository. To show an example usage of the proposed metric to text-to-image models, we sample 2000 real images which contain "dog" in its caption, from LAION-400M dataset, and use 100 prompts containing "dog" from MS COCO 2017 validation dataset for generating fake samples. For each prompt, 10 images are generated from the model. We compute the rarity scores and bottom 10 and top 10 images are shown in Figure 27 We be regarded as typical dog images. On the other hand, dog images with various backgrounds or environments have high rarity scores. Prompts the right side of Figure 27 give some guides the users of text-to-image models, for example, which prompts to use for generating more unique and fun images.
that the closed-up dog faces have low rarity scores, which can
M DENSITIES OF COMMON AND RARE SAMPLES
Table 1: KDE restuls for the real samples. 'Low NND' (nearest neighbor distance) represents for the samples with the bottom 1% NND and 'High NND' represents for the samples with the top I % NND.
Dataset
Low NND
High NND
FFHQ
1585.43 43 (1166.08)
-14842.84 (8926.26)
AFHQ-Wild
1129.75 (1801.95)
-13723.13 (6822.99)
MetFaces
959.08 (1469.12)
-16448.67 (5709.90)
We apply the kernel density estimation (KDE) on the real features, to numerically show that the common samples with low NND in Figure [ have high density in the feature space and the rare samples with high NND have low density in the feature space. We use Gaussian kernel with the bandwidth 0.2. For low NND and high NND, we use 1% of the total real samples, respectively. Table I summarizes the mean of log densities for each set of samples. In all datasets, we can that the samples with low NND, which look typical and common, have significantly higher density compared to the samples with high NND, which have strong characteristies shown in Figure] [
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N ADDITIONAL DISCUSSIONS ON RS-P SCORE
N.1 CORRELATION BETWEEN RS-P AND OOM RATIO
Table 2: Correlation coefficient between RS-p and OOM Ratio
VGG16
DINO
CLIP
Correlation Coefficient
-0.23 (0.14) 0.61 (0.05)
0.90 (0.03)
We computed the correlation coefficients between the RS-p scores and OOM ratios of five FFHQ- GAN models. For each p 10.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], we computed a correlation coefficient from five pairs of RS-p and OOM ratio for each p. Please note that the OOM ratio does not change over p. given model. For simplicity, we put mean and standard deviation (inside the parentheses) over p in Table 2 study, it shows a weak negative correlation. Further, we can see the positive/negative correlation between OOM ratio and RS-p varies over backbones. Thus, it is hard to say that OOM ratio affects RS-P in a consistent manner.
For VGGI6, which is the most preferred backbone from the user
N.2 CORRELATION BETWEEN RS-P AND FID
Table Correlation coefficient between RS-p and FID Ratio
VGG16
DINO
CLIP
Correlation Coefficient
-0.82 (0.07) 0.21 (0.06)
0.73 (0.05)
We computed the correlation coefficient between RS-p and FID scores for FFHQ-GAN models in Table The correlation coefficients are averaged over p as in Table For VGG16, we can see a strong negative correlation between RS-p and FID scores. As a lower FID score represents higher fidelity, the model with high fidelity has high rarity for VGG16. This suggests that rarity and fidelity not necessarily negatively correlated. However, always have positive correlation. For example, RS-p score and FID score show a weak positive correlation for DINO, and remarkably positive correlation for CLIP, which inversely represents negative correlation between rarity and fidelity. We conjecture that caused by the different perspective of rarity depending used backbones. The feature spaces represented by the backbones are different from each other due to the difference of their learning methods and model architectures (supervised vs. self-supervised or CNN vs. Transformers), and it might make the rarity of each backbone different. As result, negative/positive correlation may occur depending on the backbone used for feature extraction, and we leave more in-depth analysis as future research.
cannot say that rarity and fidelity
27


Reviews:
[{"review_id": "rBJko0c5h5J", "reviewer": "Reviewer_3DoB", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "summary_of_the_paper": "This paper pays attention to the vital problem in image generation --- the lack of evaluation metrics. Fidelity and diversity are two common metrics to evaluate synthetic images, while this paper considers another aspect, proposing a metric to evaluate the rarity of synthetic images. Specifically, two metrics are considered --- instance-wise rarity and model-wise rarity. Also, extensive experiments are conducted to show that the proposed metrics are reasonable. Moreover, human studies are conducted, which is more convincing.", "summary_of_the_review": "The paper considers an important problem in image generation, proposing a metric to evaluate the rarity, which is novel to me.", "strength_and_weaknesses": "Strength:\n\nS1: This paper considers an important problem in image generation, proposing a metric to evaluate the rarity of synthetic images.\n\nS2: Extensive experiments are conducted, showing that the proposed metric is reasonable. In addition, user studies make it more convincing.\n\nWeaknesses:\n\nThe author only considers the images generated by StyleGAN2, some more advanced models should be considered, in particular the models that employ texts to control image generation. Also, rarity is similar to the outlier, so I think the auth should talk about the relationships between them. Plus, can out-of-distribution or novelty detection approaches be used to evaluate instance-wise rarity?", "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": "iTFbktXgy6", "reviewer": "Reviewer_vLSP", "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": "", "summary_of_the_paper": "This paper targets on a fundamental problem in assessing the diversity or rarity of images, especially generated images. The authors first propose a novel evaluation metric, called Rarity Score (RS) to measure image-wise uncommonness, and based on RS, they further propose a model-wise rarity metric RS-p(\\theta). The authors show the effectiveness of the proposed metrics through various experiments including a user study.", "summary_of_the_review": "The studied problem in this paper is important for the vision field. The concept of “rarity score” is interesting. The proposed metric may be impactful if it has more large-scale empirical studies to verify it. Although there are some issues in current version, this is an interesting paper.", "strength_and_weaknesses": "strength:\na)\tThe problem studied in this paper is of high value. There are not many works addressing this problem in current literature. A precise diversity metric is of high demand.\nb)\tThe proposed concept is new, and the implementation is not very complex.\nc)\tThe experiments can mostly support the effectiveness of the proposed metrics.\nd)\tThe organization of the paper is well, and the writing is good.\n\nWeakness & Questions\na)\tThe proposed metrics do not consider the quality of the generated images. The rarity score is trivially high when the generated images are of low quality. The results of CelebA-HQ with Top 8 in Figure 6 can reflect it. Therefore, the proposed metrics may be biased in this situation.\nb)\tThe density of the real images is not considered in the proposed method. Consider there are two real images, i1 and i2, where the feature-space radius r1 of i1 is much larger than r2 of i2, that is to say, i1 is more rare than i2. Assume that a fake image j which has similar semantics to i1 is in both the spheres of i1 and i2. The rarity score of j is equal to r2 as r2 < r1. The fake image j is then considered as a non-rare image by the proposed metric, which is not consistent with the rarity of real image i1. This may hint the author to consider weighing samples by the density of the related real images.\nc)\tThe hypothesis that “ordinary samples would be closer to each other whereas unique and rare samples would be sparsely located in the feature space”, may need some numerical statistics to support.\nd)\tAre the proposed metrics robust to the shifts in the input images? (noise, blur, resolution, fidelity, etc.) \ne)\tI think the results of “Middle” rarity score in AFHQ-Wild in Figure 6 are the most diverse.\nf)\tFrom the result of Figure 24, FID seems more accurate regarding correlation (despite negative).\ng)\tWhat does the user-preferred backbone reflect? VGG is better than CLIP? Why?\n", "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": "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": "LzxdgUztQsH", "reviewer": "Reviewer_tiDM", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "summary_of_the_paper": "The paper proposes rarity score, a new evaluation metric for generative models, in order to measure the “rarity” or “uniqueness” of images. The metric builds on prior work such as precision/recall, coverage/density and realism scores that use nearest neighbor manifolds in pretrained VGG-like space to evaluate generative models. The rarity metric of a given image is the radius of the smallest nearest-neighbor manifold that contains the image, provided there exists such a neighborhood. The paper further proposes an extension of rarity score called RS-p to evaluate the rarity of a given generative model. \n\nQuantitative Experiments show that StyleGan2 scores the highest rarity amongst the considered generative models. The authors also perform a number of experiments to qualitatively show rare images. On training datasets, they show that diverse and uncurated datasets like Celeb-A HQ and FFHQ, have a higher proportion of rare images. Amongst generated samples, rare images consist of colorful faces and oil paintings. The rare images obtained also are dependent on the type of feature extractor considered (CLIP, VGG and DINO)\n", "summary_of_the_review": "Overall, I think this line of work is promising and interesting. I have some concerns with the experiments and details of the paper. I will consider changing if the authors address the concerns and update the draft.", "strength_and_weaknesses": "**Strengths**\n* While current work on generative model evaluation focuses on sample fidelity and diversity, this paper explores an interesting aspect that is the ability of a model to focus on modeling “niche” images of a dataset.\n* The paper is easy to read, is self-contained in the sense that it provides all the required background for the related metrics. The qualitative experiments are extensive and interesting.\n\n**Weaknesses**\n\nMy main comment is that the quantitative experiments showcasing the benefits of the “rarity score” can be more rigorous. See below for detailed feedback.\n", "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": "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": "5OEWQuW8Cj", "reviewer": "Reviewer_8Kcg", "summary": "", "questions": "", "limitations": "", "rating": 6, "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": "", "summary_of_the_paper": "This paper proposes a new evaluation metric for image generation using generative and diffusion models: rarity score. The model evaluates how rare (uncommon) the generated images are judging from the standard distribution. The instance-wise rarity score is based on k-NN and the model-wise rarity score is based on CDF. The experiments show its novelty as compared to the previous metrics and the scores are shown to align with the human perception.", "summary_of_the_review": "Please see my comments above.", "strength_and_weaknesses": "[Strength]\n+ The proposed metric, rarity score, is a novel concept.\n+ The validity of the proposed metric is quantitatively and qualitatively demonstrated.\n+ The metric has multiple applications such as rarity specification when generating images or evaluation of the generation models.\n+ The source will be shared.\n\n[Weaknesses]\n- Although it is already shown how the proposed model is different from FID and Realism scores, I still suspect the rarity score is counter correlated with the realism score. The authors might want to discuss the difference from it with facts.\n- Limitation is not discussed well. Are there any specific cases that have high rarity score but seems common to humans and vice versa? In other words, the authors might want to discuss unsuccessful cases of the proposed model.\n- Some discussions on the difference from the previous models are already given, but the motivation why a new metrics is still needed is not clear yet. Can the authors show the cases where the previous metrices do not work well but the proposed one does?\n- The experiments include humans. It was not clear whether the authors got permission of the IRB of their intuition.\n", "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": "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": {}}]

Author rebuttal:

Decision: Accept: notable-top-25%