Fréchet inception distance (FID) established itself as standard performance measuring method for generative adversarial networks (GANs). In this paper, we empirically investigate the biases that are inherited by its underlying design decision of extracting image features using the Inception v3 image classification network. As a result, we investigate how reliable FID is in terms of ranking performances of GANs. In this context, we find that FID is not aligned with human perception and exchanging Inception v3 with different image classification networks simply steers the ranking towards different biases.
Steffen Jung (Saarland Informatics Campus, Max-Planck Institute)
Margret Keuper (Uni-Mannheim)
More from the Same Authors
2021 Poster: Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders »
Amrutha Saseendran · Kathrin Skubch · Stefan Falkner · Margret Keuper