We perform this evaluation twice: once with the zt computed from ground-truth captions and once with zt
computed from descriptive synthetic captions. We do not do it for short synthetic captions as they are very
similar to ground-truth in this evaluation.
The results, shown in 4, show that both models trained on synthetic captions achieve slightly better CLIP
score performance than the baseline model when evaluated on ground-truth captions, and markedly better
performance when evaluated on descriptive synthetic captions. This indicates that there is no downside to
using synthetic captions when training text-to-image models.
It is interesting to note that the evaluation curves on synthetic captions have much lower variance. This
bolsters our theory that re-captioning can be seen as an averaging operation. Image generation models
evaluated on synthetic captions also achieve much higher net CLIP scores across all of the models trained,
Improving Image Generation with Better Captions