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Anomaly Score: Evaluating Generative Models and Individual Generated Images based on Complexity and Vulnerability
With the advancement of generative models, the assessment of generated images becomes more and more important.
Demystifying Randomly Initialized Networks for Evaluating Generative Models
Our results indicate that the features from random networks can evaluate generative models well similarly to those from trained networks, and furthermore, the two types of features can be used together in a complementary way.
TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation
The Frech\'et Inception distance is one of the most widely used metrics for evaluation of GANs, which assumes that the features from a trained Inception model for a set of images follow a normal distribution.
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