Classification models are expected to perform equally well for different
classes, yet in practice, there are often large gaps in their performance. This
issue of class bias is widely studied in cases of datasets with sample
imbalance, but is relatively overlooked in balanced datasets. In this work, we
introduce the concept of spectral imbalance in features as a potential source
for class disparities and study the connections between spectral imbalance and
class bias in both theory and practice. To build the connection between
spectral imbalance and class gap, we develop a theoretical framework for
studying class disparities and derive exact expressions for the per-class error
in a high-dimensional mixture model setting. We then study this phenomenon in
11 different state-of-the-art pretrained encoders and show how our proposed
framework can be used to compare the quality of encoders, as well as evaluate
and combine data augmentation strategies to mitigate the issue. Our work sheds
light on the class-dependent effects of learning, and provides new insights
into how state-of-the-art pretrained features may have unknown biases that can
be diagnosed through their spectra.

我们研究了不同类别之间的性能差异问题，并引入了光谱不平衡的概念来研究特征之间的类别偏差，通过构建一个理论框架，并在 11 个先进的预训练编码器上进行验证，我们展示了如何利用光谱不平衡来比较编码器的质量，评估和结合数据增强策略以缓解这个问题。