L-SWAG, a zero-shot proxy combining inverse gradient standard deviation with layer-wise activation-pattern counts, plus the LIBRA ensemble rule, outperforms prior proxies on transformer and convolutional search spaces on average.
How does topology influence gradient propagation and model perfor- mance of deep networks with densenet-type skip connec- tions? In CVPR, 2021
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers
L-SWAG, a zero-shot proxy combining inverse gradient standard deviation with layer-wise activation-pattern counts, plus the LIBRA ensemble rule, outperforms prior proxies on transformer and convolutional search spaces on average.