Supplementary Material MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
–Neural Information Processing Systems
Specifically, robustness with only ACM loss is 48.38%, the addition of soft-labels improves it to 49.53%, the addition of mixup improves it to 52.29%, and the addition of both of these components make final robustness to 56.65%. Also, note that only soft labels are not enough to transfer robustness in this case, as shown by KDOnly column. This is in line with the observations of Goldblum et al. [4]. A.4.2 Role of Intermediate Features To understand the role of low, mid, and high-level features, we performed experiments on CIFAR-10 by progressively changing blocks used for distillation. For this ablation study, we kept all the standard settings reported in the Section A.1. Our correspondence of blocks and features is as follows: block 2: low-level features; block 3: mid-level features; block 4: high-level features. Please note that block 1 corresponds to the output of the first layer only. Therefore, we do not call it low-level features.
Neural Information Processing Systems
Apr-25-2026, 03:29:33 GMT
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