Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Authors: Liam H Fowl
Micah Goldblum
Arjun Gupta
Amr Sharaf
Tom Goldstein
Venue:  (2021)
Keywords: GAN
NLP
ImageNet
generative
diversity
VAE
CelebA
language model

Abstract: Generative models are increasingly able to produce remarkably high quality images and text.  The community has developed numerous evaluation metrics for comparing generative models.  However, these metrics do not effectively quantify data diversity.  We develop a new diversity metric that can readily be applied to data, both synthetic and natural, of any type.  Our method employs random network distillation, a technique introduced in reinforcement learning.  We validate and deploy this metric on both images and text.  We further explore diversity in few-shot image generation, a setting which was previously difficult to evaluate.

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RANDOM NETWORK DISTILLATION AS A DIVERSITY METRIC FOR BOTH IMAGE AND TEXT GENERATION
Anonymous authors Paper under double-blind review
ABSTRACT
Generative models are increasingly able to produce remarkably high quality im- ages and text. The community has developed numerous evaluation metrics for comparing generative models. However, these metrics do not effectively quantify data diversity. We develop a new diversity metric that can readily be applied to data, both synthetic and natural, of any type. Our method employs random net- work distillation, a technique introduced in reinforcement learning. We validate and deploy this metric on both images and text. We further explore diversity in few-shot image generation, setting which was previously difficult to evaluate.
INTRODUCTION
State-of-the-art generative adversarial networks (GANs) able synthesize such high quality images that humans may have a difficult time distinguishing them from natural images (Brock et al., 2018 Karras al. 2019). Not only can GANs produce pretty pictures. but they are also useful for applied tasks from projecting noisy images onto the natural image manifold to generating training data (Samangouei al. 2018 Sixt et 2018 Bowles al. 2018). Similarly, massive transformer models are capable of performing cneestion-assweee
and translation (Brown et al. 2020). In order for GANs and text generators to valuable, they must generate diverse data rather than memorizing small number of samples. Diverse data should contain wide variety of semantic content, and its distribution should not concentrate around a small subset modes from the true image distribution.
A number of metrics have emerged for evaluating GAN-generated images and synthetic text. How- ever, these metrics do not effectively quantify data diversity, and they work on a small number of specific benchmark tasks (Salimans et al. 2016;; Heusel et al. (017). Diversity metries for synthetic text use only rudimentary tools and only measure similarity of phrases and vocabulary rather than semantic meaning (Zhu et al. 2018). Our novel contributions be summarized as follows:
We design a framework (RND) for comparing diversity of datasets using random network distillation. Our framework can be applied to any type of data, from images text and beyond. RND does not suffer from common problems that have plagued evaluation of gen- erative models, such vulnerability memorization, and it even be used to evaluate the diversity of natural data (not synthetic) since it does not require a reference dataset.
We validate the effectiveness of our method a controlled setting by synthetically ma- nipulating the diversity of GAN-generated images. We use the same truncation strategy employed by BigGAN to increase FID scores, and we confirm that this strategy indeed decreases diversity. This observation calls into question the usefulness of such popular metrics as FID scores for measuring diversity.
We benchmark data, both synthetic and natural, using our random distillation method. In addition to evaluating the most popular ImageNet-trained generative models and popular language models, we evaluate GANs in the data scarce regime, i.c. single-image GANs, which were previously difficult evaluate. We also evaluate the diversity of natural data.

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2 DESIGNING A GOOD DIVERSITY METRIC
Formally defining "diversity" is a difficult problem; human perception is hard to understand and does not match standard mathematical norms. Thus, we first define desiderata for a useful diversity metric, and we explore the existing literature on evaluation of generative models.
2.1 WHAT DO WE WANT FROM A DIVERSITY METRIC?
Diversity should increase as the data distribution's support includes more data. For example, the distribution of images containing brown dogs should be considered less diverse than the distribu- tion of images containing brown, black, or white dogs. While this property might seem to be good stand-alone definition of diversity, we have not yet specified what types of additional data should increase diversity measurements.
Diversity should reflect signal rather than noise. If a metric is agree with human perception of diversity, it must not be highly sensitive to noise. Humans looking at static on their television screen do not recognize that this noise is different than the last time they saw static on their screen, yet these two static noises are likely far apart with respect to lp metrics. The need to measure semantic signal rather than noise precludes using entropy-based measurements in image space without an effective perceptual similarity metric. Similarly, diversity metrics for text that rely on counting unique tokens may be sensitive to randomly exchanging words with their synonyms, or even random word swaps, without increasing the diversity of semantic content.
Quality L diversity. While some GANs can consistently produce realistic images, we do not want to assign their images high diversity measurement if they produce very little variety. In contrast, other GANs may produce a large variety of unrealistic images and should receive high diversity marks. The quality and diversity of data are not the same, and we want measurement that disentangles the two.
Metrics should be agnostic to training data. Recent single-image GANs and few-shot GANs able to generate many distinct images from very few training images (sometimes just one) (Sha-) ham et al. 2019b Clouâtre & Demers 2019). Thus, a good metric should be capable of producing diversity scores for synthetic data that are higher than those of the training set. Likewise, simply memorizing the training data should not allow a generative model achieve a maximal diversity score. Moreover, two companies may deploy face-generating models trained on two disjoint pro- prietary datasets, and we should still be able to compare the diversity of faces generated by these models without having training set access. An ideal diversity metric would allow one to collect data and measure its diversity outside of the setting of generative models.
Diversity should be measureable on many kinds of data. Measurements based on hand-crafted perceptual similarity metries or pererreeformence neural networks trained carefully on large datasets can only be used for the single type of data for which they are designed. We develop diversity concept that is adaptable to various domain, including both images and text.
2.2 EXISTING METRICS
We now review existing metrics for generative models to check if any already satisfy the above criteria. We focus on the most popular metrics before briefly discussing additional examples.
Inception Score (IS) (Salimans et al. 2016). The Inception Score is popular metric that rewards having high confidence class labels for each generated example, according to an ImageNet trained Inception V3 network. while also producing a diversity of softmax outputs across the overall set of generated images (Deng et al., 2009; Szegedy et al., 2016). While this metric does encourage generated data to be class-balanced and is not fooled by noise, IS suffers from several disqualifying problems when considered as a measure of diversity. First, it does not significantly reward diversity within classes; generative model that memorizes one image from each class in ImageNet may achieve a very strong score. Second, IS often fails when used on classes not in ImageNet and is not adaptable to settings outside of natural image classification (Barratt & Sharma, 2018). Finally, IS does not disentangle diversity from quality. The Inception Score can provide a general evaluation of GANS trained on ImageNet, but it has limited utility in other settings.
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Fréchet Inception Distance (FID) (Heusel et al. 2017). The FID score measures the Fréchet dis- tance between a Gaussian distribution fit to Inception V3 features on generated data and a Gaussian distribution fit to features ground-truth data, for example natural images on which the genera- tive model was trained. Unlike IS, FID scores compare generated data to real data, and they do not explicitly rely on ImageNet classes. Thus, FID more effectively discourages generator from memorizing one image per class. However, FID assumes that the training data is "sufficient" and does not reward producing more diversity than the training data. A second problem with FID is that it relies on either (1) ImageNet models being useful for the problem at hand or (2) the ability to acquire reference dataset and train a not disentangle diversity from quality and suffers from similar problems.
model on the problem. Like IS, FID does
Several additional metrics have attempted to address these problems. For example, precision and recall have been suggested to tease apart different aspects of a generative model's performance (Saj- jadi et al.] 2018 Kynkäänniemi et al. 20T9). However, these metrics (as used to measure diversity), along with other earlier metrics all aim to estimate the likelihood of real data under the generated distribution, and thus, require access to a reference ground-truth dataset. This makes these metrics inflexible, and these metrics achieve optimal values when generator simply memorizes the ref- erence dataset. Other examples of metrics include the Modified Inception Score (m-IS) uses a cross- entropy term resolve the fact that IS rewards models for memorizing one image per ImageNet class (uurmmurtyy et al.] 2017). However, m-IS is still beholden to the Inception label space and still prevents user from discerning diversity from quality. Boundary distortion is a method for de- tecting covariate shift in GAN distributions by comparing how well classifiers trained on synthetic data perform on ground-truth data (Santurkar et al. 2018). This measurement indicates similarity to the true data distribution rather than purely measuring diversity. and it ignores the possibility of models generating even more diverse data than the data on which they are trained. Additionally, this method assumes the user has large ground-truth dataset. Recently, classification accuracy score (CAS) was introduced to measure the performance of generative models by training classifiers on synthetic data produced by the generative models and evaluating the performance of the classifier (Ravuri& & Vinyals] 2019). Similar to the boundary distortion method, this measurement focuses its comparison on the likeness of the synthetic data distribution to the ground-truth distribution and is dependent upon having access to labeled ground-truth data.
In natural language processing, several metrics exist for evaluating text generation for dialogue, con- versational Al systems, and machine translation. Papineni et al. (2002) introduced the now widely used Bilingual Evaluation Understudy (BLEU) score as metric for evaluating the quality of ma- chine translated text. BLEU uses a modified form of precision to compare a candidate translation against multiple reference translations, where diversity ideally can be evaluated by including all plausible translations as references when computing the score. However, this requires massive an- notation cost, and it remains difficult to capture all viable translations for a given sentence, [L et al. (205)) and Xu et al.] (2018) propose counting the number of unique n-grams as measure for eval- uating the diversity of text generation tasks in conversational models, however, this metric does not account for the semantic meaning of different tokens and fails to capture paraphrases of semantically similar text. Montahaei et al. (2019) propose a joint metric for assessing both quality and diversity for text generation systems by approximating the distance of the learned generative model and the real data distribution. This metric couples both diversity and quality in a single metric. Zhu et al. (018) introduce Self-BLEUU evaluate sentence variety. Belf-BLEU measures BL.UU score for each generated sentence by considering other generations as references. By averaging these BLEU scores, a metric called SeIf-BLEU is computed where lower values indicate more diversity. How- ever, Self-BLEU remains very sensitive to local syntax, and it fails to capture global consistency and diverse semantic information in generated text.
Many other metrics exist for generative models which do not approach the problem of diversity. Our work is not the first to recognize this gap in the literature (Borji 2019).
MEASURING DIVERSITY WITH RANDOM NETWORK DISTILLATION
3.1 RANDOM NETWORK DISTILLATION
Random Network Distillation (RND) was first introduced as an exploration bonus for RL agents (Burda al. 2018). The bonus is tied to how well agent could predict the random features
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tracted from its environment. This served useful proxy for how "novel" the agent's environment was. This bonus is designed to overcome a reward trap wherein RL agents gets "stuck" in an envi- ronment. Agents could, for example, get stuck in front of an ever-changing static TV screen because the agent is rewarded for seeing a "novel" environment a new instance of random noise. With the RND bonus, the predictor network quickly learns to predict the features of the static TV screen, and the agent is not rewarded for remaining in the same environment.
3.2 OUR APPROACH
We propose a diversity measurement framework, the RND score, motivated by random network distillation. In our method, after randomly splitting the data into train and validation sets, we train predictor network to predict the feature vectors output by a randomly initialized target network. This target network is never trained. Then, the RND score is the average (normalized) generalization gap at the end of training. Intuitively, when data is diverse, we expect training data to differ significantly from validation data, while when data is not diverse, we expect training to to be similar testing data. Thus, a predictor network trained on training data from a very diverse data distribution should have harder time predicting the output of the target network on validation data. The following framework can be applied to any type of data by choosing network architecture, size of training split, and training routine appropriate for the setting. In Appendix A.44 AA.4 based comparisons between datasets are stable under these choices.
we demonstrate that RND-
Given a dataset S, target network T, and predictor network P, both with architecture A, let
MSE(T,P:5) = S
Σ FES
-
We randomly initialize target and predictor networks, 'T and P, prior to training, and we randomly split dataset S into train/validation splits, S, and S., After i epochs of training the predictor network on this mean squared error loss, let
RND,(S) MSE(T, S.; MSE (T, P.; MSE(T, P.) + MSE(T, SP:
(2)
Then, the RND score is given by
RND(S) n1
RND t=no
710 +1
where no and n denote start and end epochs for measuring RND,, respectively, and the expectation taken over random data splits and network initializations. We now discuss several design choices.
Why random networks? We leverage randomly initialized feature extractors since this keeps the RND score independent of any auxiliary datasets. Large, pre-trained feature extractors, as seen in the calculation of FID, are trained to extract useful features for classification problems on a fixed data distribution. While these feature extractors often able extract salient features from other, like distributions, the utility of pre-trained feature extractors diminishes on drastically different dis- tributions. Furthermore, we want to separate image fidelity from image diversity. Scores like FID, which rely upon pre-trained feature extractors, are sensitive to changes in image quality (Ravuri & Vinyals 2019). Random features are well-studied, both for kernel methods and neural networks (Rahimi & Recht 2008 Rudi & Rosasco 2017. Yehudai & Shamir 2019 Mei & Montanari 2019).
Why normalize the generalization gap? We normalize the generalization gap by the magnitudes of its components in order to promote scale invariance. Otherwise, data whose entries have larger magnitudes may receive a higher diversity score, despite having similar semantic content.
Consider the simple case where the target and trained predictor networks, T and P. are linear, and consider a dataset split into S. S.,. Now, consider the datasets, 25, and 28,,, formed by multiplying each element of the original datasets by 2. Since both learning problems are convex, and the target and predictor networks are of the same family, P = T and MSE(T, P: 25,) = MSE(T, P: S.) = 0. However, the generalization gaps have the property that MSE(T, P: MSE(T, P:2S,) 2 MSE(T, P; S.)) MSE(T, PP S.)] since T(2x) P(2x)=2 - -((x)). In other words, the generalization gap increases substantially if we do not normalize.
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How do estimate the RND score? We estimate the expected value in equation 3 by training the predictor network with multiple data splits. On each run, we average RND, at several epochs, no through n, to remove noise this estimate can be found in Appendix A.4 See Appendix A. A [ a sketch of the RND score computation pipeline. The exact architecture and training procedure depends
extra computational cost. Experiments concerning the stability of
the setting. For example, we use a transformer architecture to evaluate text, and we use a ResNet architecture
evaluate images (Vaswani et al. studies, can be found in appendices
2017
He al. 2016). More details about training, and ablation
A.3
and A.4
4 EXPERIMENTS
4.1 VALIDATING RND IN A CONTROLLED SETTING
In order to validate RND in controlled setting, we synthetically manipulate the diversity of GAN- generated images by truncating latent distributions. The "truncation trick" allows generative models, such as BigGAN, to improve image quality at the cost of diversity (Brock et al. 2018). Intuitively, the generator produces better looking (but less diverse) images given a latent vector coming from truncated distribution, since similar latent vectors were more likely to be sampled during training. Thus, good diversity metric should be able measure the trade-off in diversity that occurs when the latent truncation parameter is tuned. It is worth noting that unlike RND, FID scores do not always rank images according the extent of their latent truncation. Ravuri & Vinyals (2019) show that while FID scores do improve as the latent truncation parameter grows from a very small value, FID scores eventually degrade the truncation parameter increases. This behaviour may occur because FID is also sensitive to image quality characteristic that makes it ill-suited to measure diversity.
We validate our diversity measurement by generating images from three different truncation param- eters in the range found on the TensorFlow Hub implementation of BigGAN. We see in Figure | that the RND scores perfectly rank- every experiment in terms of the latent truncation parameter. Additionally, we validate the RND score by verifying that it measures signal, rather than noise, in Appendix A.4 More details about the training setup and models are in Appendix A.2 A.3
0.02
0.075
0.5
1.0
0.030
0.025
RAD Scor
0.020
0
100
200
300
400
500
600
700
800
900
Class Indles
Figure 1: Left: RND scores (1) for BigGAN images generated from latent distributions with various truncations. Right top: four images from the least diverse (most severely truncated) distribution (0.02). Right middle: four images from the second least diverse distribution (0.5). Right bottom: four images from the most diverse distribution (1.0). All images randomly selected from generated data from the ImageNet class "Academic Gown" (index 400).
We include baseline comparisons to both intra-class FID, and the recall metric proposed in Kynkäänniemi et al. (2019) in Table [[] We find that for these truncation experiments, the RND score matches both human perceived increase diversity, and the diversity measured by well established metrics.
4.2 IMAGENET GANS
ImageNet (ILSVRC2012 dataset- Russakovsky et al. (2013) is widely considered a difficult task for generative models because of its size and diversity (Brock et al. 2018). Fittingly, benchmark
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Table 1: Baseline comparisons to other metrics. Left: intra-class FID ()) scores on the truncation experiment detailed in Fig. [ Right: Recall (k=5) (1) scores on the same truncation experiment.
Class idx
0.02
0.5
1.0
Class idx
0.02
0.5
1.0
66.6105
44.5934
29.6121
0.0
0,5288
0.6337
100
42.7727
20.1884
13.7058
100
0.3461
0,6206
0.6323
200
145.0549
58.5112
30.8629
200
0.0
0.6093
0.6259
300
59.4561
28.2421
14.2526
300
0.0209
0.6201
0.6337
400
134.0554
87.4824
40.7599
400
0.0517
0.6064
0.6196
500
70.3494
45.6860
29.2421
500
0.1909
0.5795
0.6254
600
232,9096
92.5253
64.2264
600
0.2182
0,5986
0.5991
700
236.5844
122.7225
68,5625
700
0.0
0.6235
0.6245
800
136.2272
83.9329
57.2190
800
0.0
0.5322
0.6318
900
78.18263
49.4285
33.6281
900
0.0
0.5927
0.6015
well-known generative models of varying recency on several ImageNet classes. In our comparison, include BigGAN (128 x 128). Self-Attention GAN (SAGAN), and AC-GAN (Brock al. 2018 Zhang et al. 2019; Odena et al.) 2017). We find that the newer generative models, BigGAN and diverse images than the older AC-GAN model. However, metric is able to detect class where the SAGAN model collapses and produces homogeneous images (see Figure 2). Note that the training procedure in calculating RND scores varies from that of the truncation experiments due to the differences in settings such as image size. More details on this experiment can be found in appendix section A.3
SAGAN, generally produce
0.08
BipGAN ACCAN
0.06
Scone
END
0.02
0.00
Obass Indox
Figure 2: Left: RND scores (f) for different ImageNet 128 x 128 GANs. Right top: four im- ages from BigGAN. Right middle: four images from ACGAN. Right bottom: four images from SAGAN. All images randomly selected from generated data from the ImageNet class "Tench" (in- dex 0). The RND score emphasizes the poor diversity in this class of SAGAN data.
4.3 IMAGENET CLASSES
Because our metric does not rely on comparing generated samples to ground-truth data, unlike FID, are able to compare the diversity of natural data across classes ImageNet. We compare di- versity across the 10 ImageNet classes on which we measured the diversity of ImageNet generators. We present these results in Figure 3 We find that there is wide range of diversity among ImageNet classes. RND scores for the classes, along with randomly selected example images from the least and most diverse classes included in Figure
4.4 SINGLE- GANs
Popular generative models often leverage large amounts of data during training to generate high quality and diverse images. However, growing body of work aims to generate images when train- ing data is scarce. Recent work has demonstrated unconditional image generation learned from single image (Shaham et al. 2019a| Hinz et al., 2020). These models leverage the idea that images can be decomposed into patches which can be re-configured distribute information to parts of

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0.04
PMD 300%
0.02
0.00
Clocs Indee
Figure 3: Left: RND scores (t) for ten different ImageNet classes. We observe a range of diversity measurements and display images from the most and least diverse classes. Right top: four images taken from the least diverse ImageNet class measured ("Tibetan terrier" index 200). Right bottom: four images taken from the most diverse class measured ("Hook/Claw" index 600).
synthetic image where it did not exist in the single training sample. In this domain, practitioners hope the generated data will be more diverse than the ground-truth data. FID fails to accurately measure diversity in this setting because these methods do not seek replicate the distribution of data they were trained on. Additionally, in the data-scarce setting, underlying distributions may be very different than the distribution of ImageNet data used to train the feature extractor leveraged in FID. Since our metric does not depend on ground-truth dataset, we avoid these problems and are able to measure the diversity images generated by few-shot generative models.
We benchmark two recent methods in the data dcta-e regime: SinGAN (Shaham et al. 2019a) and ConSinGAN (Hinz et al. 2020). The authors of SinGAN propose Single-Image FID (SIFID) to measure performance in this domain. SIFID calculates the Fréchet distance between Gaussian dis- tributions fit to early layer features (compared with last layer features of the extractor used by FID). Thus, SIFID also suffers from the problem that simply reproducing the training image minimizes the score. Furthermore, because only one image is used in the feature extraction, SIFID was later found to exhibit very high variance across generated images and does not provide valuable distinction between "better" and "worse" images (Hinz et al. 2020).
To benchmark these models, we generate variety of images from randomly selected fixed training images, and we compare the diversity measurements. Results of the experiments can be found in Figure 4 While ConSinGAN claims significant advantages training time over SinGAN, find that in many cases, SinGAN actually produces more diverse data than ConSinGAN, confirming the suspicions of Hinz al. (2020). More details can be found in Appendix A.3
ConSiNGAN
0.030
SinGaN
220 Scow
0.015
0.010
0.005
100
200
300
400
300
600
700
800
500
Class Indies
Figure 4: Left: RND scores (1) comparing single-image GANs. Right top: four randomly selected images generated from a single ImageNet training image by SinGAN. Right bottom: four randomly selected images generated from single ImageNet training image by ConSinGAN. Images from the class "Tiger Beetle" (index 300).

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4.5 CELEBA
In addition measuring the diversity of variety of generative models ImageNet data, also benchmark several generative models, including a VAE, on unconditional (w.r.t face attributes) CelebA 128 128 images. The CelcbA dataset contains over 200K images of celebrities with differ- ent facial attributes (Liu et al. 2018). We measure the diversity of faces generated by RealnessGAN Adversarial Latent Autoencoder (ALAE), and Progressive Growing GAN (PGAN) (Xiangli et al,, 2020 Pidhorskyi et al. 2020; Karras et al. (017)) Our results indicate that newer generative models, like RealnessGAN, outperform the older PGAN model, which tends to produce faces the same pose and scale, on diversity of generated samples (see Figure
0.030
200 Scie
I
0.005
0.000
Gensrative Medel
Figure 5: Left: RND scores (t) for different CelebA 128 128 generative models. Right top: four images randomly taken from the most diverse data generated by RealnessGAN, Right middle: four random images generated by PGAN. Right bottom: four random images generated by ALAE.
4.6 RND IN A NATURAL LANGUAGE PROCESSING SETTING
In addition to measuring the diversity generative models for images, we also benchmark several natural language processing models. We seek to answer the following questions empirically: 1. How does RND measure diversity in a controlled setting where we manipulate the size of the model vocabulary? 2. How does RND rank diversity for naturally and synthetically generated text from models of varying capacity?
To answer these questions, we conduct experiments on the WikiText dataset (Merity et al. 2016). The WikiText language modeling dataset is collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. Following Xu al. (2018), we use the number of distinct tokens in the vocabulary as surrogate for controlling diversity. We synthetically manipulate the diversity of the text by truncating the size of the vocabulary. Similar to our experi- ments in |suseettion 4.1 this "truncation trick" allows us to improve the text quality the cost of diversity. We validate our diversity measurement by evaluating text from the WikiText dataset using five different vocabulary sizes: 5k, 10k, 15k, 20k, 25k, where we keep track of the top-k tokens in the text, and replace the least frequent tokens with an voccbul-y (unknown) token.
Additionally, compare the natural text from WikiText synthetic text from the OpenAl GPT- (Radford et al. 2018) and GPT-2 (Radford et al. 2019) models (see Appendix A.3 for details).
We use Tranformer model (Vaswani et al. 2017) implemented in Pytorch (Paszke al.) 2019) as the model architecture for the predictor and target networks. We use the CMU Book Summary Dataset (Bamman & Smith 2013) generate input prompts for the GPT models.
The results are presented in Figure 6, along with RND scores in Appendix Table 4 eralization gap versus the number of training epochs. The figure shows the results for the controlled experiment where we vary the size of the vocabulary. Here, we see that RND captures the correct ranking of diversity with larger vocabulary sizes achieving higher scores than less diverse smaller vocabulary. Also included in the figure are diversity comparisons for the natural versus syn- thetically generated text. We observe that RND captures the correct ranking of diversity with the more diverse natural text scoring higher than the synthetic text and the more diverse generations from the stronger GPT-2 model scoring higher than generations from the older GPT-1 model.
We plot the gen-
with
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SK
10K
13K
20K
25%
GPT-1
GPP33
WklTted
Gcankatta
Epodts
Figure 6: Left: Generalization gap for different vocabulary sizes. RND captures the correct ranking of diversity with larger vocabulary sizes achieving higher scores than less diverse, smaller vocabu- lary text. Right: Generalization gap for natural and synthetic text. RND scores the more diverse natural text higher than the synthetic text, with the stronger GPT-2 scoring higher than GPT-1,
DISCUSSION
We present the need for diversity metric and introduce a novel framework for comparing diversity based on technique from reinforcement learning Random Network Distillation. Our framework avoids many of the pitfalls of existing metrics when comes to measuring diversity. Furthermore, our metric is flexible and dataset-agnostic, allowing to measure diversity in several settings in- cluding natural images and text, along with setting for which current metrics are poorly suited: few-shot image generation. We validate our method in number of controlled scenarios and present results on a wide variety of well-known generative models.
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Under review as a conference paper at ICLR 2021
A APPENDIX
A.I ALGORITIM
Algorithm 1: Random Network Distillation Score
Require: Dataset S, training set size k, number of runs r; start and end RND score epochs, no and n, and network architecture A.
for j in {1.....r) r) do
Randomly initialize target network T and predictor network P. Randomly split S into training set S. with k elements, and validation set S.
for i in 1, ,,..., do Calculate RNN D; in eq.
Update parameters of P to minimize eq.
end
Calculate RND Li
RND, D,, the average RND value for run j.
end
Return: +EA=1RN RND
A.2 GENERATOR DETAILS
ImageNet For validating our method on BigGAN truncation data, we generate im- ages from given class at resolution 256 x 256 using pretrained weighs from the BigGAN TF Hub at a truncation value of 1.0 unless otherwise specified. (https: 7/colab, research. google www. wtwphhhubbbessooff...... http://www...oo/////////////eeaa
examples / htwolaabbbggga...oa gew.aatieenrnnnnnnn with tf hub. ipynb). For comparing Ima- geNet generative models, we generate images at the 128 128 resolution since this is the resolution at which majority of the models we test produce images. We use pretrained SAGAN, and ACGAN The SAGAN baseline model
models taken from https: //pp::///qie thub. com/ Lyakavaagaaa achieves an FID score of 16.39. The ACGAN baseline model achieve an FID score of 24.72. For the BigGAN generated images, we again use pretrained weights from the BigGAN TF Hub. Details about the FID scores of BigGAN can be found in BBooc et al. (2018). For 128 x 128 GAN comparisons, we use 100 training points as compared with 200 for the full resolution, BigGAN benchmark
Single-Image GANs To test single-image GANs, we fix randomly selected base image IDs for each ImageNet class tested, and then generate 200 images from each base image. Then, collect all the generated images from multiple ground-truth images, and randomly shuffle these into a training and validation set. All code taken from the respective github pages for Shaham al. 2019b): Hinz et al. (2020). We perform runs with a training split of 100 images because of the relative lack of diversity of single-image GANs compared with classical GANs trained a large amount of data. Experiments were averaged over 20 runs, on a ResNet18 with final 10 epochs averaged out of 40 training epochs.
CelebA We use pretrained models for each of the CelebA experiments. Pretrained models for ALAE, RealnessGAN can be found on their respective github pages. We use the pretrained PGAN model found on the pytorch GAN zoo github / hub.. com/ facebookkeseare bytorch_GAN_zae ALAE requires a base image to generate data, so we randomly sample (un- conditionally) images from CelebA to generate samples from ALAE.
NLP: described Radford et al. (2018), the GPT-1 model consists of a 12-layer decoder trans- former with 12 attention heads and 3, 072-dimensiona hidden states, GPT-2 (a successor to GPT- was trained to predict the next word 40GB of Internet text. GPT-2 is a larger transformer- language model trained on dataset of 8 million web pages.
we use word representations of size 400, feedforward layers with inner dimensions 400, multi-head attention with 4 attention heads. The model is optimized with Adam (Kingma & Bal 2014) using a learning rate 0.001. The learning rate was selected using grid search independent subset of the WikiText-2 dataset using a grid search with value
For the experiments in Figure 6
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ranging from 0.01 to 1c-7. We use 4 transformer encoder layers, and we simple linear decoder for computing the scores over vocabulary tokens.
A.3 EXPERIMENT DETAILS
Unless otherwise stated, we run experiments with the following hyperparameter choices:
Net: ResNet-18
Learning rate: 0.01
Epochs to average score: 10
Epochs: 50
Number of runs: 40
Training number: 200
Optimizer: SGD with momentum 0.9
We perform ablation studies these choices in appendix A.4 It is worth noting that within diversity is can be compared, but comparing diversity scores across experiments is diffieult because of changes in the setting such as image size, training/validationn split, architecture. We show stability of these choices within a given experiment in appendix A.4 but the choices do affect comparisons across experimental settings.
fixed comparison like comparing ImageNet GANs in Figure 2
Normalization: To improve comparability across different models, we normalize data to have mean=0, std=1 for each channel before training the predictor network. We estimate the mean and std of the distribution using the generated samples.
Measurement time: The measurement time varies depending on how much training data used, and how many runs the measurement is averaged over. For 20 runs on an ImageNet class at full resolution, with the predictor net trained for 50 epochs, the RND score takes approximately 3.85 gpu-hours to complete on a NVIDIA GV100.
Hardware: Experiments were rum on hetergeneous mixture of hardware consisting of NVIDIA GV100 and NVIDIA RTX 2080 Ti gpus.
A.4 ADDITIONAL. EXPERIMENTS
Does RND measure signal noise? A good diversity measurement should be able distinguish between meaningful features in data, and random noise. As RND was able to distinguish these in the setting of RL, we expect the RND diversity measurement to able distinguish these as well. To test this, we generate a fixed set of 3-channel noise data (uniform between [0, I]), and compare the RND score to a randomly selected ImageNet class (see Figure We find natural images are far more diverse than random noise. Averaged over 40 runs.
Epochs: The RND score is calculated by training predictor net for n epochs, and averaging over the last nn epochs. We perform ablation studies on the number of epochs the predictor net is trained in Figure 8 We find that while the scale of the RND scores changes slightly, the ordering of the diversity scores does not change.
Additionally, we test the effect of the choice ng on the RND score Figure 9 We find there leeway in this choice of hyperparameter averaging over 10, 15, 20 epochs all produces the same ordering in truncation diversity experiments.
Number of Runs: Each run in the calculation of RND corresponds to instance of randomly initialized target and predictor networks. We test the effects of the number runs used in calculating the RND score, finding that the relative ordering of diversity for given class is preserved (see Figure
10
Confidence Intervals: For the truncation studies in Figure I perform 50 runs resulting in fidence intervals given in Tables 3 The user may tune the number runs as hyperparameter with the tradeoff between time and confidence interval width.
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0.040
800 Score
0.025
0.000
Randon Noke
Naturdl
Figure Left: RND scores (1) for random noise, and natural images taken from randomly selected ImageNet class. Right top: four images taken from the ImageNet class measured ("Slot" index 800). Right bottom: four images of random noise used to calculate RND score for random noise.
0.899
0.025
BNO
0.015
0.03
0.005
0.000
0.909
350
200
402
500
650
700
402
120
300
500
600
820
Class Index
Class Index
Figure 8: Ablation on epochs used to train the predictor net. Truncation experiments repeated as in Left: predictor net trained for 30 epochs. Right: predictor trained for 40 epochs. While see variation in the scores, the relative ordering is preserved across different networks.
0.000
0.000
0.025
0.225
BNO S006
0.025
0.015
0.013
9.005
9.20
0.000
Figure 9: Ablation on epochs used to average in calculating RND score. Truncation experiments repeated as in [ Left: RND calculated with final 15 epochs. Right: RND calculated with final 20 epochs. The relative ordering is preserved across different networks.
Training/Vas Val split: A hyperparameter choice make when calculating the RND score is the num- ber of datapoints the training set used for distillation. Heuristically, the amount of training data will make difference in the RND score in the limiting cases. Too few training data, and the
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0.39
0.639
0.023
0.929
0.020
PNO Scoe
0.235
0.010
0.003
0.003
200
450
650
870
450
650
800
Class Inder
Class Index
Figure 10: Ablation on runs used to calculate RND score. Truncation experiments repeated as in ! Left: 30 runs used in the calculation. Right: 40 runs used in the calculation. The relative ordering preserved across number runs.
Table 2: RND (1) scores with confidence intervals for different truncation parameters.
Class idx
0.02
0.5
1.0
0
0.0025 + 0.0011
0.0144 + 0.0059
0.0293 + 0.0053
100
0.0064 + 0.0015
0.0184 + 0.0059
0.0268 + 0.0056
200
0.0074 + 0.0016
0.0246 0.0063
0.0257 0.0044
300
0.0045 + 0.0008
0.0068 + 0.0065
0.0136 = 0.0061
400
0.0032 = 0.0011
0.0145 0.0059
0.0231 0.0073
500
0.0047 = 0.0013
0.0187 + 0.0074
0.0250 = 0.0083
600
0.0049 + 0.0017
0.0223 0.0098
0.0271 0.0097
700
0.0047 + 0.0016
0.0205 + 0.0081
0.0254 + 0.0061
800
0.0016 + 0.0007
0.0173 0.0036
0.0232 + 0.0072
900
0.0024 + 0.0012
0.0295 + 0.0083
0.0372 = 0.0120
generalization gap will be large for any dataset, diverse or not. Too many training data, and the generalization gap for diverse sets will be small provided the network accurately learns the features of the target network. This because the distribution will be "saturated" with training data, and the predictor network will be able perform well unseen data. However, find that the relative ordering of diversity is stable for a wide band of training splits (see Figure
Network Architecture:
Table RND (1) scores for different ImageNet classes.
Class idx
RND score
0.0358 + 0.0135
100
0.0411 + 0.0134
200
0.0263 + 0.0072
300
0.0352 + 0.0110
400
0.0431 + 0.0124
500
0.0302 0.0076
600
0.0445 0.0126
700
0.0367 0.0114
800
0.0389 + 0.0088
900
0.0409 + 0.0106
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0.05
Sree
0.03
0.000
200
450
650
800
450
650
8t0
Class Inder
Class Index
Figure 11: Ablation on training split. Truncation experiments repeated for selected indices as in ! Left: training data set 100. Right: training data set 500. While variation in the scale of the scores, the relative ordering is preserved across different training splits.
We test the sensitivity of the RND score a change network architecture. Results of these experiments are presented in Figure 12 We find that there is change in the RND score across architecture, but the relative ordering datasets of known variation in diversity remains unchanged. Changes in architecture may be necessary when dealing with more or less complex data, as well as settings such as text. Because of this, we stress that RND is best viewed as an intra-setting diversity measurement because of this.
0.040
0.02
0.92
0.035
3.0
0.5
9.023
0.025
END Scoee
0.215
0.035
9.305
0.005
200
450
650
800
200
400
620
860
class Index
Class Index
Figure 12: Ablation on network used calculate RND score. Truncation experiments repeated for selected indices as in [ Left: Alexnet results (Kriheesky et al. 2012). Right: ResNet34 Results. While we see variation in the scores, the relative ordering preserved across different networks.
NLP RND Scores: The numerical RND scores for the experiments described in Figure pre- sented in Table
Table 4: RND Scores for NLP. Left:
Validating RND on NLP with different vocabulary sizes. Right: Comparing different generated texts natural text.
Vocabulary Size
RND Score
Model
RND Score
1.0537
10k
1,4256
GPT-1 GPT-2 WikiText-2
1.5561
15k
1.5877
1.6439
20k
1.6446
1.6947
25k
1.6860
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A.5 VISUALIATION
0.890
0.899
0.025
Coneratattion Cap Darmakae
9.025
0.035
0.03
0.21
0,015
9.005
0.505
Figure 13: Visualizing generalization gap during training. Left: Index 0. Right: Index 100
0.02
0.0255
0.5
0.02
1.0
Ceneraaat Gap (n0mm Inammatee 82440
0.053
0.025
00100
0.035
0.0075
0.220
0.055
0.0925
0.005
0.0005
0.000
+0.0325
Spach
Spoch
Figure 14: Visualizing generalization gap during training. Left: Index 200. Right: Index 300
0.525
0.02
mm
0.02
10
Caner antraaaainnG. Gap Inrmatzdd
0.315
0.00
0.000
9.505
0.500
0.300
10
50
10
23
30
40
50
Spach
Spoch
Figure 15: Visualizing generalization gap during training. Left: Index 400. Right: Index 500
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MM
0.023
Geeerr Dap
0.020
0.005
0.500
Epoch
Figure 16: Visualizing generalization gap during training. Left: Index 600. Right: Index 700
0.225
0.02
0.02
0.5
Devoostertinn ONn (noraattee)
0.500
10
(ntmaaties)
100
2.03
0.010
0.905
0.03
-0.305
10
50
10
23
30
40
50
Epach
Epoch
Figure 17: Visualizing generalization gap during training. Left: Index 800. Right: Index 900
Caneralization gap of different Imagelei classes
200
Figure 18: Generalization gap during training on 10 ImageNet classes.
18


Reviews:
[{"review_id": "oS68VEzJwOI", "reviewer": "AnonReviewer5", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes that generalization performance of distillations of random networks can be used as a good metric for the diversity of a data set: as a data set gets more diverse, it should be harder to learn to mimic a random computation on that data set.  After defining the metric, the paper compares it to FID in its ability to distinguish truncated GAN output, and applies it to compare different generative architectures and training settings, to compare different data sets such as imagenet classes, and to measure diversity of natural language model outputs.\n\nThe strength of the paper in its interesting viewpoint, that diversity can be viewed as the difficulty of a random learning task.  The framework and concept is promising.  It is good to see that, unlike FID, it detects the loss of diversity as a generator is truncated, without mixing the measurement with precision. And the comparisons of different models and data sets is interesting.\n\nHowever, in proposing that RND should be used as a diversity metric, the paper does not sufficiently compare the proposed method to previously proposed alternatives.  The paper should establish that the metric is meaningful and useful.  Three are three main issues.\n\n1. What is being measured by RND?  Beyond just the operational definition of how the metric is collected?  In what situations would measuring this quantity be expected to differ from other metrics, and what strength weaknesses do the proposed metric have compared to other methods?  It seems possible that the idea of diversity-through-learning complexity could have an interesting theoretical definition, but the paper does not attempt a theoretical characterization of exactly what quantity would ideally be estimated by the RND procedure.\n\n2. How does it compare to previously proposed metrics? A comparison to FID is done, but there are many other approaches for measuring diversity.  E.g., see [Borji 2018] for a survey of a large number of alternative metrics for diversity, many of which are designed to measure recall of a generative model compared to a known diverse ground truth.  Or see [Sajjadi 2018] or even the Parzen windows of [Goodfellow 2014].  Since RND measures diversity without need for a ground truth, it could be argued that RND allows new measurements.  But to establish the metric is sound, it should first be compared to a variety of recall metrics where a ground truth is known.  Comparisons should be done with different types of recall measures, and also different types of data distributions including toy examples where differences in diversity are easy to understand.\n\n3. It is a goal for RND to match human assessments of diversity?  It is argued that some complexity (such as noise) is uninteresting and should not be included in a diversity metric - but this seems to imply that the goal is to measure just the diversity that would be interesting or informative to a human.  If this is the goal, then the performance of RND should be compared to human assessments of diversity.\n\nThe idea and the topic of the paper is interesting and strong, but needs further development to argue that the proposed measure is meaningful and useful, especially compared to other approaches.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "iCM76vG6yV", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "In this paper, the authors introduce a new quantitative diversity measure advocating its usage for generative models evaluation. In a nutshell, to measure the diversity of a particular set, the authors split it into disjoint train/val subsets and learn a DNN to predict the outputs of another randomly initialized DNN on the train set. Then the generalization gap of the trained DNN is computed on the unseen val subset, and the normalized value of this gap (averaged over several splits/initializations) is considered as a diversity measure.\n\nPros:\n\n(1) The authors tackle an important problem since the established measures like FID are known to sacrifice diversity in favor of perceptual quality.\n\n(2) The proposed measure is novel, the usage of random networks in a new context sounds interesting.\n\nCons:\n\n(1) The authors do not relate their measure to the very relevant line of existing works on measuring diversity via Precision/Recall:\n\nAssessing Generative Models via Precision and Recall. NeurIPS 2018\n\nImproved Precision and Recall Metric for Assessing Generative Models. NeurIPS 2019\n\nReliable Fidelity and Diversity Metrics for Generative Models, ICML 2020\n\nWithout explicit highlighting of RND's advantages over the Recall/Coverage measures, I cannot recommend to accept the paper.\n\n(2) The computation of RND requires several DNN trainings, which is time-consuming. This makes RND inconvenient for broad usage, and almost impossible to use in day-to-day research, e.g., for monitoring the training progress.\n\n(3) I am not quite convinced by the experiments, which support the RND applicability. For me, the most sensible experiment is in section 4.1, which shows that aggressive truncations decrease RND. Sections 4.2/4.5 show that newer GAN models typically achieve higher RND compared to older ones, but I cannot consider this as strong evidence, since we do not know if the advantage of newer models comes from diversity rather than perceptual quality.\n\nOverall, my current recommendation is (4), mostly because of missing a crucial part of related work and unconvincing experiments.\n\n::::::Post-Rebuttal update::::::\n\nAfter reading the new revision, I decided to keep my initial score. I do not consider the need of groundtruth real data for metric computation as a strong disadvantage. The authors report some numbers on Recall in Table 1 but it only shows that Recall is consistent with RND, being much cheaper to compute. Therefore, I do not see any reason to prefer RND over established diversity metrics.", "extra_scores": {}, "extra_text": {}}, {"review_id": "PGKJAwvCBn", "reviewer": "AnonReviewer1", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "RND as a Diversity Metric\nThis paper proposes a new modality-independent diversity measure for generative models and examines this across image and natural language generation.  The idea repurposes an exploration technique from reinforcement learning:  random network distillation. The method produces a diversity score of data by splitting it into train and validation partitions. Then, a predictor network is trained via a mean-square error (MSE) to predict the resulting features of passing the train data through a fixed, randomly initialized target network. The diversity measure is then computed as the normalized MSE difference between the train and validation partitions. The intuition of the method is that if the train partition is diverse, then we would expect the predictor network to generalize well in predicting validation target features. If it’s not diverse, we would expect a large gap.\n\nI recommend acceptance. This appears to be a useful advance as a diversity measure that works across different modalities. I’m not aware of prior work doing this. The largest weakness, IMO, is that the work doesn’t do enough study into the importance and nature of the random target network. I imagine this is a critical decision (e.g. don’t use a MLP for a vision target network) and, if the authors want this to be widely adopted by different communities, should provide further guidance on this.\n\nNotes:\n* This has obvious failure modes. For instance, if the target network was a 0-network (all inputs mapped uniformly to a 0-vector), this trivially fails as a diversity measure. This paper should address more details about the requisite nature of the target network. This is the biggest weakness of this paper and I would upgrade my score with a more thorough scientific investigation here.  “The exact architecture and training procedure depends on the setting. For example, we use a transformer architecture to evaluate text, and we use a ResNet architecture to evaluate images (Vaswani et al., 2017; He et al., 2016).”\n* I enjoyed Section 2.1 “What do we want from a diversity metric?”. Capturing the notion of diversity, distinct from information-theoretic measures, is an important property.\n\nQuestions to authors:\n* What is the importance of the random network architecture? How does the “architectural prior” impact the efficacy of your approach as a data diversity measure?\n* Could you improve the clarity of Section 3.1? It reads as, “The bonus is tied to how “novel” an agent’s environment is - as measured by the distillation loss between a fixed, random target network, and a predictor network trained to mimic the features of the target network.”  I found myself re-consulting the original paper to make sure I knew what was going on. I found Section 3.2 to be much clearer.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "Kzm-q0M2i59", "reviewer": "AnonReviewer4", "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper applies random network distillation (RND) as a method for quantifying how diverse samples from a generative model are. Samples from the generative model (or any dataset) are used to train a neural network to mimic a randomly initialized network. Intuitively, this is a more difficult task on a more diverse dataset, and so the distillation loss can be interpreted as a measure of diversity. The authors argue that this approach has advantages over other diversity metrics because it can capture semantic diversity and does not require a second reference dataset.\n\nStrengths of paper:\n+ This article is well-written. The motivation and approach are very clear. \n+ The technique is demonstrated in several different domains, including image generation, text generation, and one-shot GANs.\n+ The approach is intuitive and agrees with qualitative notions of diversity across each domain it was tested in.\n\nWeaknesses of paper:\n- The original contribution is minimal as RND distillations loss is a known technique for quantifying exploration. The main originality comes from identifying it as a way to also quantify diversity in generative dataset.\n- The distillation loss metric is not compared to other diversity metrics. This would help demonstrate that the RND score is better aligned with diversity than other standard metrics.\n-  The claim that the RND score captures semantic diversity is not well supported. This deserves some scrutiny as the RND is a random feature detector, so it is not clear why it will generally favor semantic diversity. There is an experiment in the appendix to show that the RND score was greater for natural images than random ones,  but it is unclear whether other statistics of the random noise were controlled to make this a fair comparison. This should be expanded to determine that it generalizes. \n\nOverall, while the paper has some merits, it needs to compare its metrics to other available ones to better make its argument.\n\nComment:\nThe NLP model needs to be initialized with real text. It would be interesting to dive deeper into how context affects the diversity of the generated text.", "extra_scores": {}, "extra_text": {}}, {"review_id": "JWvuZCMVgyE", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "I understand the authors goal on developing a diversity metric and evaluate models diversity. However, the concept of diversity is related to multiple factors and I don't agree to define diversity independent of memorization: a good memorization of diverse data also contributes to the diversity of models and sometimes good memorization suggests high capacity of model thus may lead to high diversity.\nIn the approach proposed, the memorization concept is implicitly wrapped into the size of the predictor model.  But there is no analysis on the effect of the predictor model sizes on the diversity scores.\nFor the experiment section, the evaluations are rather non-systematic, only a few categories' RND scores are shown. A table of overall performance will be good.\nMeasuring diversity of models is an important task, while this paper provides some interesting discussion on defining the diversity and proposed method to measure it. but the definition needs a bit refinement and the the author failed to prove that the propose method is a systematic metric on the diversity of models (only showed specific categories is not enough).", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes the generalization performance of distillation from random networks as a metric of diversity, named RND. Intuitively, the more diverse the generated datasets, the more difficult it should be for a model to learn a random computation. The reviewers agree that the metric has a novel perspective. Unfortunately, the paper is not sufficiently developed to be accepted at this point. It is currently missing a number of experiments that would demonstrate that this metric is indeed a measure of diversity:

1.) RND shows sensitivity to the truncation trick in GANs (for images), and limiting the size of vocabulary in text, but does not show sensitivity to any other changes in diversity (such as human judgment of diversity)
2.) It does not compare to previous metrics of diversity, of which there are many
3.) How sensitive is RND to architecture choice.
4.) It is non-obvious to what extent the metric is sensitive to image/text quality

Strong metrics should demonstrate lack of "failure modes", as the utility of a metric is its inability to be gamed. Currently, the paper does not demonstrate this property, though I imagine that more work will help clear up the strengths and weaknesses of the metric.  As a result, I can only recommend rejection.
Decision: Reject