human alignment benchmarks (perceptual similarity, gloss, robustness, shape-texture)

Joint Energy-Based Models Reveal a Generative-Discriminative Sweet Spot for Human-Aligned Vision

Researchers use Joint Energy-Based Models (JEMs) to isolate the effect of learning objective—independent of architecture, scale, and data—on human alignment in visual representations. By varying a single mixing coefficient between discriminative and generative training, they evaluate models across six human-alignment benchmarks and find that alignment peaks at intermediate points on the generative-discriminative continuum rather than at either extreme. The results suggest that hybrid objectives combining categorical structure from discriminative learning with input-structure sensitivity from generative learning yield the most human-like visual behavior. This challenges the framing of generative vs. discriminative as a binary choice for building human-aligned vision systems.