Neither hype nor gloom do DNNs justice
This addresses the debate over DNNs' role in vision science for researchers, but it is incremental as it critiques existing views without introducing new methods.
The paper argues that deep neural networks (DNNs) are evolving models in vision science, and their limitations are temporary, advocating for balanced consideration of explanation, prediction, and image-computability as model desiderata.
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other.