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.

翻译：深度神经网络（DNNs）在物体识别任务中，即使在平衡的训练数据条件下，也常常表现出对某些类别的偏见。这些偏见形成的内在机制尚不明确。受人类视觉系统通过分层处理解耦物体流形以实现物体识别的启发，我们提出了一个几何分析框架，将DNNs中特定类别的感知流形的几何复杂度与模型偏见联系起来。我们的研究结果表明，几何复杂度的差异可能导致不同类别间的识别能力产生差异，从而引入偏见。为了支持这一分析，我们提出了感知流形几何库，该库专为计算感知流形的几何特性而设计。