“A sense of beauty” in untrained deep neural networks:

The sense of facial beauty has long been observed in both infants and nonhuman primates, yet the neural mechanisms of this phenomenon are still not fully understood. The current study employed generative neural models to produce facial images of varying degrees of beauty and systematically investigated the neural response of untrained deep neural networks (DNNs) to these faces. Representational neural units for different levels of facial beauty are observed to spontaneously emerge even in the absence of training. Furthermore, these neural units can effectively distinguish between varying degrees of beauty. Additionally, the perception of facial beauty by DNNs relies on both configuration and feature information of faces. The processing of facial beauty by neural networks follows a progression from low-level features to integration. The tuning response of the final convolutional layer to facial beauty is constructed by the weighted sum of the monotonic responses in the early layers. These findings offer new insights into the neural origin of the sense of beauty, arising the innate computational abilities of DNNs.

Saving this for later, because I wonder how closely this ‘beauty’ tuning maps onto known properties of facial beauty in humans (i.e. inverse distance from prototypical faces—symmetry, averageness, etc).