Package: prdc
Reliable Fidelity and Diversity Metrics for Generative ModelsPaper: Reliable Fidelity and Diversity Metrics for Generative ModelsMuhammad Ferjad Naeem1,3*, Seong Joon Oh2*, Yunjey Choi1,
Youngjung Uh1, Jaejun Yoo1,4Work done at Clova AI Research* Equal contribution1Clova AI Research, NAVER Corp.2Clova AI Research, LINE Plus Corp.3Technische Universität München4EPFLDevising indicative evaluation metrics for the image generation task remains an open problem.
The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score.
Because it does not differentiate thefidelityanddiversityaspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those properties separately.
In this paper, we show that even the latest version of the precision and recall (Kynkäänniemi et al., 2019) metrics are not reliable yet. For example, they fail to detect the match between two identical distributions, they are not robust against outliers, and the evaluation hyperparameters are selected arbitrarily. We proposedensity and coveragemetrics that solve the above issues. We analytically and experimentally show that density and coverage provide more interpretable and reliable signals for practitioners than the existing metrics.1. BackgroundPrecision and recall metricsPrecision and recall are defined below:where the manifold is the defined asis the ball around the pointxwith radiusr.is the distance to the kth-nearest neighbour.Density and coverage metricsDensity and coverage are defined below:Why are DC better than PR?Precision versus Density.Because of the real outlier sample, the manifold is overestimated. Generating many fake samples around the real outlier is enough to increase the precision measure.
The problem of overestimating precision (100%) is resolved using the density estimate (60%).Recall versus Coverage.The real and fake samples are identical across left and right.
Since models often generate many unrealistic yet diverse samples, the fake manifold is often an overestimation of the true fake distribution.
In the figure above, while the fake samples are generally far from the modes in real samples, the recall measure is rewarded by the fact that real samples are contained in the overestimated fake manifold.2. UsageInstallationpip3installprdcExampleTest 10000 real and fake samples form the standard normal distribution N(0,I) in 1000-dimensional Euclidean space.
Set the nearest neighbourk=5. We compute precision, recall, density, and coverage estimates below.importnumpyasnpfromprdcimportcompute_prdcnum_real_samples=num_fake_samples=10000feature_dim=1000nearest_k=5real_features=np.random.normal(loc=0.0,scale=1.0,size=[num_real_samples,feature_dim])fake_features=np.random.normal(loc=0.0,scale=1.0,size=[num_fake_samples,feature_dim])metrics=compute_prdc(real_features=real_features,fake_features=fake_features,nearest_k=nearest_k)print(metrics)Above test code will result in the following estimates (may fluctuate due to randomness).{'precision':0.4772,'recall':0.4705,'density':1.0555,'coverage':0.9735}3. MiscellaneousReferencesKynkäänniemi et al., 2019. Improved precision and recall metric for assessing generative models. Neurips 2019.LicenseCopyright (c) 2020-present NAVER Corp.

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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.Cite this work@article{ferjad2020ganeval,
title = {Reliable Fidelity and Diversity Metrics for Generative Models},
author = {Naeem, Muhammad Ferjad and Oh, Seong Joon and Uh, Youngjung and Choi, Yunjey and Yoo, Jaejun},
year = {2020},
journal = {arXiv},
}