Aligning machine and human visual representations across abstraction levels.

ABSTRACT: Deep neural networks have achieved success across a wide range of applications, including as models of human behaviour and neural representations in vision tasks^1,2. However, neural network training and human learning differ in fundamental ways, and neural networks often fail to generalize as robustly as humans do^3,4, raising questions regarding the similarity of their underlying representations. We need to determine what is missing for modern learning systems to exhibit more human-aligned behaviour. Here we highlight a key misalignment between vision models and humans: whereas human conceptual knowledge is hierarchically organized from fine- to coarse-scale distinctions (for example, ref. ⁵), model representations do not accurately capture all these levels

SUBMITTER: Muttenthaler L 

PROVIDER: S-EPMC12611773 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature