Deep neural networks (DNNs) often exhibit biases toward certain categories during object recognition, even under balanced training data conditions. The intrinsic mechanisms underlying these biases remain unclear. Inspired by the human visual system, which decouples object manifolds through hierarchical processing to achieve object recognition, we propose a geometric analysis framework linking the geometric complexity of class-specific perceptual manifolds in DNNs to model bias. Our findings reveal that differences in geometric complexity can lead to varying recognition capabilities across categories, introducing biases. To support this analysis, we present the Perceptual-Manifold-Geometry library, designed for calculating the geometric properties of perceptual manifolds.
View on arXiv@article{ma2025_2502.11809,
title={ Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling },
author={ Yanbiao Ma and Bowei Liu and Boyuan Gao and Wei Dai and Jiayi Chen and Shuo Li },
journal={arXiv preprint arXiv:2502.11809},
year={ 2025 }
}