This is the Windows app named Reliable Metrics for Generative Models whose latest release can be downloaded as Initialrelease.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
Reliable Metrics for Generative Models
DESCRIPTION:
Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising 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 the fidelity and diversity aspects 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 propose density and coverage metrics that solve the above issues.
Features
- Precision and recall metrics
- Density and coverage metrics
- Test 10000 real and fake samples form the standard normal distribution N(0,I) in 1000-dimensional Euclidean space
- Generating many fake samples around the real outlier is enough to increase the precision measure
- Set the nearest neighbour k=5
- Precision, recall, density, and coverage estimates
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/reliable-met-gen-mod.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.