The authors introduce MIND (Monge Inception Distance), a new metric for evaluating generative models that fixes major problems with the widely used FID (Fréchet Inception Distance). MIND uses sliced Wasserstein distance—averaging many one-dimensional optimal transport problems—instead of comparing high-dimensional Gaussian statistics, which makes it much more sample-efficient, faster to compute, and robust to adversarial attacks like moment-matching.
Main takeaways:
- FID estimates high-dimensional means and covariances, which requires tons of samples and is fragile; MIND avoids this by using sliced optimal transport (sorting in 1D).
- MIND with 5,000 samples performs as well as FID with 50,000 samples, a 10× improvement in sample efficiency.
- MIND is two orders of magnitude faster to compute than FID.
- Even at 1,000–2,000 samples, MIND remains highly informative for rapid model iteration, and it correlates well with FID while being more discriminative and attack-resistant.