Why the Inception Score might trick us when judging generative models
People use the Inception Score to judge image-making models, but it can be misleading.
It sounds simple: higher score means better images.
Yet in practice the number often says little about real quality, and can push teams to chase metrics instead of real progress.
Some models get high scores for the wrong reasons, while others that look better to humans score lower.
That makes comparisons noisy, confusing, and sometimes wrong.
We need to test models in smarter ways, and be more careful when reading a single number.
Try using different checks, look at example images, and ask if the score matches what you actually want.
If researchers keep relying on one metric, the field moves slower and ideas get lost.
This is a call to rethink how we compare systems, so future work grows on strong, honest ground, not just on a flashy number that sounds impressive but often fails to deliver.
Read article comprehensive review in Paperium.net:
A Note on the Inception Score
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