2022/11/15 by Justin Dulay, Dulay, Justin, Walter J. Scheirer +1
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2211.07885
openalex publication_date 2022/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent trends in the machine learning community show that models with fidelity toward human perceptual measurements perform strongly on vision tasks. Likewise, human behavioral measurements have been used to regularize model performance. But can we transfer latent knowledge gained from this across different learning objectives? In this work, we introduce PERCEP-TL (Perceptual Transfer Learning), a methodology for improving transfer learning with the regularization power of psychophysical labels in models. We demonstrate which models are affected the most by perceptual transfer learning and find that models with high behavioral fidelity -- including vision transformers -- improve the most from this regularization by as much as 1.9% Top@1 accuracy points. These findings suggest that biologically inspired learning agents can benefit from human behavioral measurements as regularizers and psychophysical learned representations can be transferred to independent evaluation tasks.