1 Introduction
Capturing the essence of a manifold, defined implicitly by a set of samples, into a computational “recipe” that allows generation of novel samples is a central goal of generative models. While the quality of results from generative adversarial networks (GAN) Goodfellow2014
, variational autoencoders (VAE)
Kingma2014 VanDenOord2016A ; VanDenOord2016B , and likelihoodbased models Dinh2016 ; Kingma2018 have seen rapid improvement recently Karras2017 ; Grover2018 ; Tolstikhin2018 ; Miyato2018B ; brock2018biggan ; Karras2019 , the automatic evaluation of these results continues to be challenging.When modeling a complex manifold for sampling purposes, two separate goals emerge: individual samples drawn from the model should be faithful to the examples (they should be of “high quality”), and their variation should match that observed in the training set. The most widely used metrics, such as Fréchet Inception Distance (FID) Heusel2017 , Inception Score (IS) Salimans2016B , and Kernel Inception Distance (KID) KID2018 , group these two aspects to a single value without a clear tradeoff. We illustrate by examples that this makes diagnosis of model performance difficult. For instance, it is interesting that while recent stateoftheart generative methods brock2018biggan ; Kingma2018 ; Karras2019 claim to optimize FID, in the end the (uncurated) results are almost always produced using another model that explicitly sacrifices variation, and often FID, in favor of higher quality samples from a truncated subset of the domain Marchesi2017 .
Meanwhile, insufficient coverage of the underlying manifold continues to be a challenge for GANs. Various improvements to network architectures and training procedures tackle this issue directly Salimans2016B ; Metz2016 ; Karras2017 ; Zinan2017 . While metrics have been proposed to estimate the degree of variation, these have not seen widespread use as they are subjective Arora2017b , domain specific Metz2016 , or not reliable enough Odena2017 .
Recently, Sajjadi et al. Sajjadi2018 proposed a novel metric that expresses the quality of the generated samples using two separate components: precision and recall. Informally, these correspond to the average sample quality and the coverage of the sample distribution, respectively. We discuss their metric (Section 1.1) and characterize its weaknesses that we later demonstrate experimentally. Our primary contribution is an improved precision and recall metric (Section 2) which provides explicit visibility of the tradeoff between sample quality and variety. We will make the source code of our metric publicly available.
We demonstrate the effectiveness of our metric using two recent generative models (Section 3), StyleGAN Karras2019 and BigGAN brock2018biggan . We then use our metric to analyze several variants of StyleGAN (Section 4) to better understand the design decisions that determine result quality, and identify new variants that improve the stateoftheart. We also perform the first principled analysis of truncation methods Marchesi2017 ; Kingma2018 ; brock2018biggan ; Karras2019 . Finally, we extend our metric to estimate the quality of individual generated samples (Section 5), offering a way to measure the quality of latent space interpolations.
1.1 Background
(red). (b) Precision is the probability that a random image from
falls within the support of , i.e., . (c) Recall is the probability that a random image from falls within the support of , i.e., .Precision and recall are metrics commonly used to evaluate, e.g., binary classification and information retrieval systems. In the latter case, precision measures how large proportion of the documents returned by a search engine are relevant to the user, and recall measures how large proportion of all relevant documents were returned. Traditionally, these metrics are defined only for sets, but Sajjadi et al. Sajjadi2018 present a formulation based on distributions that is suited for evaluating generative models. In this context, the generator acts as the retrieval engine, and the manifold of realistic images (as defined by the training set) corresponds to the set of relevant items. Precision thus maps to the proportion of generated images that are realistic, and recall measures to which extent the generator covers the realm of realistic images.
Sajjadi et al. Sajjadi2018 also describe a practical method for computing precision and recall of a generative network. They embed training images and generated images in a highdimensional feature space using a pretrained Inception network Szegedy2014 and employ minibatch means clustering on the resulting points. Precision and recall are then computed based on the populations of training images and generated images in the clusters. However, as we will show in Section 3.1, this method has some shortcomings. In particular, it tends to give overoptimistic results, and it cannot correctly interpret situations where a large number of generated samples are packed together. This can happen, e.g., as a result of mode collapse or truncation. Furthermore, calculating “pure” precision and recall is problematic with Sajjadi et al.’s metric — both of them tend to be very close to in most scenarios — so they propose visualizing the metric using a plot over different relative weightings between precision and recall. These plots should not be confused with traditional precisionrecall curves where each point represents a different method, parameter value, etc.
Our goal is to measure pure precision and recall without weighting. In the notation of Sajjadi et al., these correspond to and , i.e., the volume of one distribution within the support of the other distribution. See Figure 1 for an illustration.
While precision and recall quantify two highly relevant aspects of a generative model, it should be noted that they do not directly compare the densities of the generated and real distributions. For example, an ideal facegenerating model should reproduce the distributions of age, gender, pose, ethnicity, etc., from the training set, but precision and recall are more concerned about the extents of the distributions than their relative densities. Fréchet Inception Distance Heusel2017
fits multivariate Gaussian distributions to the featurespace embeddings of real and generated images, and computes the Wasserstein2 distance between these two Gaussians. As such, it is a proper distance metric between the distributions and is able to quantify the difference between the densities of real and generated images. The drawback is that a low FID may indicate high precision (realistic images), high recall (large amount of variation), or anything in between.
2 Improved precision and recall metric using nearest neighbors
We will now describe our improved precision and recall metric that does not suffer from the weaknesses listed in Section 1.1. The key idea is to form explicit nonparametric representations of the manifolds of real and generated data, from which precision and recall can be estimated.
Similar to Sajjadi et al. Sajjadi2018 , we draw real and generated samples from and
, respectively, and embed them into a highdimensional feature space using a pretrained classifier network. We denote feature vectors of the real and generated images by
and , respectively, and the corresponding sets of feature vectors by and . We take an equal number of samples from each distribution, i.e., .For each set of feature vectors , we estimate the corresponding manifold in the feature space as illustrated in Figure 2. We obtain the estimate by calculating pairwise Euclidean distances between all feature vectors in the set and, for each feature vector, forming a hypersphere with radius equal to the distance to its th nearest neighbor. Together, these hyperspheres define a volume in the feature space that serves as an estimate of the true manifold. To determine whether a given sample is located within this volume, we define a binary function
(1) 
where returns th nearest feature vector of from set . In essence, provides a way to determine whether a given image looks realistic, whereas provides a way to determine whether it could be reproduced by the generator. We can now define our metric as
(2) 
In Equation (2), precision is quantified by querying for each generated image whether the image is within the estimated manifold of real images. Symmetrically, recall is calculated by querying for each real image whether the image is within estimated manifold of generated images. See Appendix A for pseudocode.
In practice, we compute the feature vector for a given image by feeding it to a pretrained VGG16 classifier Simonyan2014vgg and extracting the corresponding activation vector after the second fully connected layer. Brock et al. brock2018biggan show that the nearest neighbors in this feature space are meaningful in the sense that they correspond to semantically similar images. Zhang et al. Zhang2018metric , on the other hand, use the intermediate activations of multiple convolutional layers of VGG16 to define a perceptual metric for image corruptions that they show to correlate well with human judgments. We have tested both approaches and found that Brock at al.’s feature space works considerably better for the purposes of our metric. We hypothesize that this is because Zhang et al.’s approach implicitly places a lot of focus on spatial structure, which is appropriate for assessing image corruptions. In our case, however, the image manifolds are sparsely sampled and we cannot expect to find exact matches in terms of spatial structure. This favors choosing a feature space that eschews spatial structure and focuses on semantic content instead.
Like FID, our metric is weakly affected by the number of samples taken. Since it is standard practice to quote FIDs with 50k samples, we adopt the same design point for our metric as well. The size of the neighborhood, , is a compromise between covering the entire manifold (large values) and overestimating its volume as little as possible (small values). In practice, we have found that higher values of increase the precision and recall estimates in a fairly consistent fashion, and lower values of decrease them, until they start saturating at 1.0 or 0.0. Tests with various datasets and GANs showed that is a robust choice that avoids saturating the values most of the time. Thus we use and in all our experiments except the BigGAN measurements (Section 3.2) where approximately 1300 real images per class were available.
3 Precision and recall of stateoftheart generative models
In this section, we demonstrate that precision and recall computed using our method correlate well with perceived quality and variation of generator output distributions, and compare our metric with Sajjadi et al.’s method Sajjadi2018 as well as the widely used FID metric Heusel2017 . For Sajjadi et al.’s method, we use 20 clusters and report and as proxies for precision and recall, respectively, as recommended by the authors. We examine two stateoftheart generative models, StyleGAN Karras2019 trained with the FFHQ dataset, and BigGAN brock2018biggan
trained on ImageNet
imagenet .3.1 StyleGAN
Figure 3 shows the results of various metrics in four StyleGAN setups. These setups exhibit different amounts of truncation and training time, and have been selected to illustrate how the metrics behave with varying output image distributions.
Setup A is heavily truncated, and the generated images are of high quality but very similar to each other in terms of color, pose, background, etc. This leads to high precision and low recall, as one would expect. Moving to setup B increases variation, which improves recall, while the image quality and thus precision is somewhat compromised. Setup C is the FIDoptimized configuration in Karras2019 . It has even more variation in terms of color schemes and accessories such as hats and sunglasses, further improving recall. However, some of the faces start to become distorted which reduces precision. Finally, setup D preserves variation and recall, but nearly all of the generated images have low quality, indicated by much lower precision as expected.
In contrast, the method of Sajjadi et al. Sajjadi2018 indicates that setups B, C and D are all essentially perfect, and incorrectly assigns setup A the lowest precision. Looking at FID, setups B and D appear almost equally good, illustrating how much weight FID places on variation compared to image quality. This is also evidenced by the extremely high FID of setup A. Setup C is ranked as clearly the best by FID despite the obvious image artifacts. The ideal tradeoff between quality and variation depends on the intended application, and it is unclear which application might favor, e.g., setup D where practically all images are broken over setup B that produces highquality samples at a lower variation. The primary benefit of our metric is that it provides explicit visibility on this tradeoff and allows quantifying the suitability of a given model for a particular application.
Figure 4 illustrates the effects of truncation Marchesi2017 ; brock2018biggan ; Kingma2018 ; Karras2019 on precision and recall using a single StyleGAN generator. With our method, the maximally truncated setup () has zero recall but high precision. As truncation is gradually removed, precision drops and recall increases as expected. In contrast, the method of Sajjadi et al. reports both precision and recall increasing as truncation is removed, contrary to the expected behavior. We hypothesize that the difficulties are a result of truncation packing a large number of generated images into a small region in the embedding space. This may result in clusters that contain no real images in that region, and ultimately causes the metric to incorrectly report low precision. The tendency to underestimate precision can be alleviated by using fewer clusters, but doing so leads to overestimation of recall. Our metric does not suffer from this problem because the manifolds of real and generated images are estimated separately, and the distributions are never mixed together.
3.2 BigGAN
Brock et al. recently presented BigGAN brock2018biggan , a highquality generative network able to synthesize images for ImageNet imagenet . Imagenet is a diverse dataset containing 1000 classes with approximately 1300 training images for each class. Due to the large amount of variation within and between classes, generative modeling of ImageNet has proven to be a challenging problem Miyato2018 ; Zhang2018sagan ; brock2018biggan .
Brock et al. brock2018biggan list several ImageNet classes that are particularly easy or difficult for their method. The difficult classes often contain precise global structure or unaligned human faces, or they are underrepresented in the dataset. The easy classes are largely textural, lack exact global structure, and are common in the dataset. Dogs are a noteworthy special case in ImageNet: with almost a hundred different dog breeds listed as separate classes, there is substantially more training data for dogs than for any other class, making them artificially easy. To a lesser extent, the same applies to cats that occupy 10 classes.
Figure 5 illustrates the precision and recall for some of these classes over a range of truncation values. We notice that precision is invariably high for the suspected easy classes, including cats and dogs, and clearly lower for the difficult ones. Brock et al. state that the quality of generated samples increases as more truncation is applied, and the precision as reported by our method is in line with this observation.
Recall paints a more detailed picture. It is very low for classes such as “Lemon” or “Broccoli”, implying much of the variation has been missed, but FID is nevertheless quite good for both. Since FID corresponds to a Wasserstein2 distance in the feature space, low intrinsic variation implies low FID even when much of that variation is missed. Correspondingly, recall is clearly higher for the difficult classes. Based on visual inspection, these classes have a lot of intraclass variation that BigGAN training has successfully modeled. Dogs and cats show similar recall compared to the difficult classes, and their image quality and thus precision is presumably boosted by the higher amount of training data.
4 Using precision and recall to analyze and improve StyleGAN
Generative models have seen rapid improvements recently, and FID has risen as the de facto standard for determining whether a proposed technique is considered beneficial or not. However, as we have shown in Section 3, relying on FID alone may hide important qualitative differences in the results and it may inadvertently favor a particular tradeoff between precision and recall that is not necessarily aligned with the actual goals. In this section, we use our metric to shed light onto some of the design decisions associated with the model itself and perform the first principled analysis of truncation methods. We use StyleGAN Karras2019 in all experiments, trained with FFHQ at .
4.1 Network architectures and training configurations
To avoid drawing false conclusions when comparing different training runs, we must properly account for the stochastic nature of the training process. For example, we have observed that FID can often vary by up to between consecutive training iterations with StyleGAN. The common approach is to amortize this variation by taking multiple snapshots of the model at regular intervals and selecting the best one for further analysis Karras2019 . With our metric, however, we are faced with the problem of multiobjective optimization branke2008multiobjective : the snapshots represent a wide range of different tradeoffs between precision and recall, as illustrated in Figure 6a. To avoid making assumptions about the desired tradeoff, we identify the Pareto frontier, i.e., the minimal subset of snapshots that is guaranteed to contain the optimal choice for any given tradeoff.
Figure 6b shows the Pareto frontiers for several variants of StyleGAN. The baseline configuration (A) has a dedicated
minibatch standard deviation
layer that aims to increase variation in the generated images Karras2017 ; Zinan2017 . Using our metric, we can confirm that this is indeed the case: removing the layer shifts the tradeoff considerably in favor of precision over recall (B). We observe that regularization Mescheder2018 has a similar effect: reducing the parameter by shifts the balance even further (C). Karras et al. Karras2017 argue that their progressive growing technique improves both quality and variation, and indeed, disabling it reduces both aspects (D). Moreover, we see that randomly translating the inputs of the discriminator by pixels improves precision (E), whereas disabling instance normalization in the AdaIN operation Huang2017 , unexpectedly, improves recall (F).Figure 6c shows the best FID obtained for each configuration; the corresponding snapshots are highlighted in Figure 6a,b. We see that FID favors configurations with high recall (A, F) over the ones with high precision (B, C), and the same is also true for the individual snapshots. The best configuration in terms of recall (F) yields a new stateoftheart FID for this dataset. Random translation (E) is an exceptional case: it improves precision at the cost of recall, similar to (B), but also manages to slightly improve FID at the same time. We leave an indepth study of these effects for future work.
4.2 Truncation methods
Many generative methods employ some sort of truncation trick Marchesi2017 ; brock2018biggan ; Kingma2018 ; Karras2019 to allow trading variation for quality after the training, which is highly desirable when, e.g., showcasing uncurated results. However, quantitative evaluation of these tricks has proven difficult, and they are largely seen as an adhoc way to finetune the perceived quality for illustrative purposes. Using our metric, we can study these effects in a principled way.
StyleGAN is well suited for comparing different truncation strategies because it has an intermediate latent space in addition to the input latent space . We evaluate four primary strategies illustrated in Figure 7a: A) generating random latent vectors in via the mapping network Karras2019 and rejecting ones that are too far from their mean with respect to a fixed threshold, B) approximating the distribution of latent vectors with a multivariate Gaussian and rejecting the ones that correspond to a low probability density, C) clamping lowdensity latent vectors to the boundary of a higherdensity region by finding their closest points on the corresponding hyperellipsoid eberly2011distance , and D) interpolating all latent vectors linearly toward the mean Kingma2018 ; Karras2019 . We also consider three secondary strategies: E) interpolating the latent vectors in instead of , F) truncating the latent vector distribution in along the coordinate axes Marchesi2017 ; brock2018biggan , and G) replacing a random subset of latent vectors with the mean of the distribution. As suggested by Karras et al. Karras2019 , we also tried applying truncation to only some of the layers, but this did not have a meaningful impact on the results.
Figure 7b shows the precision and recall of each strategy for different amounts of truncation. Strategies that operate in yield a clearly inferior tradeoff (E, F), confirming that the sampling density in
is not a good predictor of image quality. Rejecting latent vectors by density (B) is superior to rejecting them by distance (A), corroborating our Gaussian approximation as a viable proxy for image quality. Clamping outliers (C) is considerably better than rejecting them, because it provides better coverage around the extremes of the distribution. Interpolation (D) appears very competitive with clamping, even though it ought to perform no better than rejection in terms of covering the extremes. The important difference, however, is that it affects all latent vectors equally — unlike the other strategies (A–C) that are only concerned with the outliers. As a result, it effectively increases the average density of the latent vectors, countering the reduced recall by artificially inflating precision. Random replacement (G) takes this to the extreme: removing a random subset of the latent vectors does not reduce the support of the distribution but inserting them back at the highestdensity point increases the average quality.
^{1}^{1}1Interestingly, random replacement (G) actually leads to a slight increase in recall. This is an artifact of our NN manifold approximation, which becomes increasingly conservative as the density of samples decreases.Our findings highlight the fact that recall alone is not enough to judge the quality of the distribution — it only measures the extent. To illustrate the difference, we replace recall with FID in Figure 7c. Our other observations remain largely unchanged, but interpolation and random replacement (D, G) become considerably less desirable as we account for the differences in probability density. Clamping (C) becomes a clear winner in this comparison, because it effectively minimizes the Wasserstein2 distance between the truncated distribution and the original one in . We have inspected the generated images visually and confirmed that clamping appears to generally yield the best tradeoff.
5 Estimating the quality of individual samples
While our precision metric provides a way to assess the overall quality of a population of generated images, it yields only a binary result for an individual sample and therefore is not suitable for ranking images by their quality. Here, we present an extension of the classification function (Equation 1) that provides a continuous estimate of how close a given sample is to the manifold of real images.
We define a realism score that increases the closer an image is to the manifold and decreases the further an image is from the manifold. Let be a feature vector of a generated image and a feature vector of a real image from set . Realism score of is calculated as
(3) 
This is a continuous extension of with the simple relation that iff . In other words, when , the feature vector is inside the (NN induced) hypersphere of at least one .
With any finite training set, the NN hyperspheres become larger in regions where the training samples are sparse, i.e., regions with low representation. When measuring the quality of a large population of generated images, these underrepresented regions have little impact as it is unlikely that too many generated samples land there — even though the hyperspheres may be large, they are sparsely located and cover a small volume of space in total. However, when computing the realism score for a single image, a sample that happens to land in such a fringe hypersphere may obtain a wildly inaccurate score. Large errors, even if they are rare, would undermine the usefulness of the metric. We tackle this problem by discarding half of the hyperspheres with the largest radii. In other words, the maximum in Equation 3 is not taken over all but only over those whose associated hypersphere is smaller than the median. This pruning yields an overconservative estimate of the real manifold, but it leads to more consistent realism scores. Note that we use this approach only with , not with .
Figure 8 shows example images from BigGAN with high and low realism. In general, the samples with high realism display a clear object from the given class, whereas the object is often distorted to unrecognizable for the low realism images. Appendix B provides more examples.
5.1 Quality of interpolations
An interesting application for the realism score is to evaluate the quality of interpolations. We do this with StyleGAN using linear interpolation in the intermediate latent space as suggested by Karras et al. Karras2019 . Figure 9 shows four example interpolation paths with randomly sampled latent vectors as endpoints. Paths A appears to be located completely inside the real manifold, path D completely outside it, and paths B and C have one endpoint inside the real manifold and one outside it. The realism scores assigned to paths A–D correlate well with the perceived image quality: Images with low scores contain multiple artifacts and can be judged to be outside the real manifold, and vice versa for highscoring images. See Appendix B for additional examples.
We can use interpolations to investigate the shape of the subset of that produces realisticlooking images. In this experiment, we sampled without truncation 1M latent vectors in for which , giving rise to 500k interpolation paths with both endpoints on the real manifold. It would be unrealistic to expect all intermediate images on these paths to also have , so we chose to consider an interpolation path where more than 25% of the intermediate images have as straying too far from the real manifold. Somewhat surprisingly, we found that only 2.4% of the paths crossed unrealistic parts of under this definition, suggesting that the subset of on the real manifold is highly convex. We see potential in using the realism score for measuring the shape of this region in with greater accuracy, possibly allowing the exclusion of unrealistic images in a more refined manner than with truncationlike methods.
6 Conclusion
We have demonstrated through several experiments that the separate assessment of precision and recall can reveal interesting insights about generative models. Our method for measuring these metrics appears to correlate well with perceived image quality and variation, and it responds in a consistent manner to techniques that alter the tradeoff between the two.
Using our metric, we have identified previously unknown training configurationrelated effects in Section 4.1, raising the question whether trunction is really necessary if similar tradeoffs can be achieved by modifying the training configuration appropriately. We leave the indepth study of these effects for future work. Finally, we believe that the more detailed understanding enabled by our metric, in addition to the previous techniques for estimating distribution quality Heusel2017 ; KID2018 , will prove valuable for further improving generative models.
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Appendix A Pseudocode and implementation details
Algorithm 1 shows the pseudocode for our method. The main function PrecisionRecall evaluates precision and recall for given sets of real and generated images, and , by embedding them in a feature space defined by (lines 2–3) and estimating the corresponding manifolds using ManifoldEstimate (lines 4–6). The helper function ManifoldEstimate takes two sets of feature vectors as inputs. It forms an estimate for the manifold of and counts how many points from are located within the manifold. Estimating the manifold requires computing the pairwise distances between all feature vectors and, for each , tabulating the distance to its th nearest neighbor (lines 9–11). These distances are then used to determine the fraction of feature vectors that are located within the manifold (lines 13–17). Note that in the pseudocode feature vectors are processed one by one on lines 9 and 14 but in a practical implementation they can be processed in minibatches to improve efficiency.
We use NVIDIA Tesla V100 GPU to run our implementation. A highquality estimate using 50k images in both and takes 8 minutes to run on a single GPU. For comparison, evaluating FID using the same data takes 4 minutes and generating 50k images () with StyleGAN using one GPU takes 14 minutes. We will make our implementation opensource.
Appendix B Quality of samples and interpolations
Figure 10 shows BigGANgenerated images for which the estimated realism score (Section 5) is very high or very low. Images with high realism score contain a clear object from the given class, whereas lowscoring images generally lack such object or the object is distorted in various ways.
Figure 11 presents further examples of high and low quality interpolations (Section 5.1). Highquality interpolations consist of images with high perceptual quality and coherent background despite the endpoints being potentially quite different from each other. On the contrary, lowquality interpolations are usually significantly distorted and contain incoherent patterns in the image background.
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