Unsupervised learning reveals interpretable latent representations for translucency perception
Fig 2
Experiment 1: Real-versus-generated discrimination.
(A) Examples of real photographs and model-synthesized images of soaps. The “generated” soaps were synthesized by embedding a real photograph into the W+ latent space of the trained StyleGAN2-ADA using the pSp encoder. We used 150 real photographs and 150 generated images as stimuli for Experiments 1 and 2. (B) The procedure of Experiment 1. (C) Overall correct and error rates of judging real and generated images. The error rate of 50% indicates pure guessing. (D) Distribution of the percentage of real and generated images misjudged by the observers. The x-axis represents the percentage of observers misjudging an image and the y-axis is the percentage of images being misjudged. Gray color represents data of real images and green represents data of generated images.