Trained in this way, the model could estimate whether a fresh image contained more or fewer than a given number of objects (Nature Neuroscience, DOI: 10.1038/nn.2996).

A differentiable counting function, such as contrastive language–image pre-training (CLIP)-count, was employed to achieve accuracy. This function estimated object counts within generated images.