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Few-shot Image Generation via Adaptation-Aware Kernel Modulation

NeurIPS 2022 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Cross-domain few-shot with transformers · 16 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add importance probing (select kernels to modulate) vs modulating all kernels (ablation)AdAM · FFHQ->AFHQ-CatFID: 82.41 -> 58.07Helped
add importance probing vs modulating all kernels (ablation)AdAM · FFHQ->BabiesFID: 54.46 -> 48.83Helped
modulate important kernels with KML instead of freezing them (ablation)AdAM · FFHQ->AFHQ-CatFID: 61.60 -> 58.07Helped
modulate important kernels with KML instead of freezing them (ablation)AdAM · FFHQ->BabiesFID: 50.81 -> 48.83Helped
add importance probing to EWC (ablation)EWC · FFHQ->BabiesFID: 79.93 -> 70.80Helped
add importance probing to EWC (ablation)EWC · FFHQ->AFHQ-CatFID: 74.61 -> 66.35Helped
add importance probing to AdaFM (ablation)AdaFM · FFHQ->BabiesFID: 62.90 -> 55.64Helped
add importance probing to AdaFM (ablation)AdaFM · FFHQ->AFHQ-CatFID: 64.44 -> 60.04Helped
use source-only knowledge-preserving methods (EWC, CDC, DCL) instead of plain fine-tuningTGAN baseline · FFHQ->AFHQ-CatFID: 64.68 -> 176.21 (CDC)Hurt
use CDC/DCL instead of plain fine-tuning on close-proximity targetTGAN baseline · FFHQ->BabiesFID: 101.58 -> 56.48 (DCL)Helped
replace DCL with AdAM on distant targetDCL · FFHQ->AFHQ-WildFID: 115.93 -> 36.87Helped
add importance probing to AdAM (ablation)AdAM · FFHQ->BabiesIntra-LPIPS: 0.613 -> 0.590Hurt

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