Lower local intrinsic dimension predicts vision-model alignment and generalization

measured in 1 paper

Yu et al. estimate local intrinsic dimension via the Levina-Bickel maximum-likelihood kNN estimator across a pool of 91 vision models (ConvNeXt, ResNet, ResMLP, ViT), with a 51-model architecture-balanced subset for cross-architecture comparison [yu-etal-2026-local-intrinsic-dimension-alignment] Local ID is significantly negatively correlated with AI-AI representational alignment, AI-brain alignment (fMRI, Natural Scenes Dataset), and ImageNet-1K generalization [yu-etal-2026-local-intrinsic-dimension-alignment] The correlation is strongest at small (local) neighborhood scale K and weakens toward global estimates, with a matched-subsample control confirming genuine local structure [yu-etal-2026-local-intrinsic-dimension-alignment] Increasing model capacity and training-data scale systematically reduces local ID (local ID vs log-parameters Corr=-0.756, p<0.001) [yu-etal-2026-local-intrinsic-dimension-alignment] PCA is used only as a top-300-component control to equalize ambient dimensionality, not as the ID estimator, and robustness is checked with the MOM and MADA estimators [yu-etal-2026-local-intrinsic-dimension-alignment]

Structure

Intrinsic-dimension profile across depth

Context

local intrinsic dimension (Levina-Bickel MLE, K-nearest-neighbor), multi-scale ID sweep (local vs. global neighborhood size K), AI-AI and AI-brain alignment via ridge-regression R^2 (PCA-300 projected), local geometry vs. sample-size control (matched subsampling), scaling reduces local intrinsic dimension

Confirmed in models

ConvNeXt (image classifier, various sizes, ImageNet) · ConvNeXt, ResNet (image classifier, various depths) · ResNet, Vision Transformer (ViT, image classifier, various sizes, ImageNet) · Vision Transformer (ViT), ResMLP (image classifier, various sizes, ImageNet) · ResMLP

Method

Local Intrinsic Dimensionality (LID) under perturbation, PCA

Papers

Local Intrinsic Dimension of Representations Predicts Alignment and Generalization in AI Models and the Human Brain — Yu, Junjie, Ma, Wenxiao, Wei, Chen, Zhang, Jianyu, Deng, Haotian, Deng, Zihan, Liu, Quanying2026 · arXiv:2601.22722