d360a502598a4b64b936683b44a5523a-Supplemental.pdf

This supplementary material presents additional results and descriptions of our approach that are not included in the main paper due to the page limit. Afterwards, we illustrate the implementation details including architecture designs and hyper-parameters for training in Section D. Lastly, Section E We again observe the superiority of our method regardless of the corruption types.Dataset Ratio (%) LfF [1] Ours Corrupted CIFAR-10 Type 0 0.5 33.95 Table 1: Image classification accuracy evaluated on unbiased test sets of Corrupted CIFAR-10 Type 0 and Type 1 with varying ratio of bias-conflicting samples. Best performing results are marked in bold. Similar to Figure 1 of the main paper, columns and rows correspond to those images where the bias attribute ( i.e., gender) and the intrinsic attribute ( i.e., age) are extracted, As mentioned in Section 5 of the main paper, we define'age' as either'young' or'old' In addition, we observe that the ages of reconstructed images change as the row changes. We also provide training details of the decoder for BFFHQ in Section D.4.