Appendix: Combating Representation Learning Disparity with Geometric Harmonization

We provide our source codes to ensure the reproducibility of our experimental results. Below we summarize several critical aspects w.r .tthe The datasets we used are all publicly accessible, which is introduced in Appendix E.1. For long-tailed subsets, we strictly follows previous work [29] on CIFAR-100-L T to avoid the bias attribute to the sampling randomness. On ImageNet-L T and Places-L T, we employ the widely-used data split first introduced in [44]. All the experiments are conducted on NVIDIA GeForce RTX 3090 with Python 3.7 and Pytorch 1.7.