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Efficient Parametrization of Multi-Domain Deep Neural Networks

CVPR 2018 · Architectures and efficiency › Pruning and efficient tuning · approach: Parameter-efficient transfer analysis · 45 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace series adapters with parallel adaptersResNet-26 adapters · Visual Decathlonmean top-1 accuracy: 77.17 -> 78.07Helped
apply adapters in early, middle, and late blocks rather than only one block (ablation)Parallel adapters on ResNet-26 · Visual Decathlonmean top-1 accuracy: 75.32 / 76.40 / 77.40 -> 78.07Helped
add adapters to the early block (ablation)Parallel adapters on ResNet-26 · Visual Decathlonmean top-1 accuracy: 75.32 -> 78.07Helped
add adapters to the middle block (ablation)Parallel adapters on ResNet-26 · Visual Decathlonmean top-1 accuracy: 76.40 -> 78.07Helped
add adapters to the late block (ablation)Parallel adapters on ResNet-26 · Visual Decathlonmean top-1 accuracy: 77.40 -> 78.07Helped
adapt both convolutional layers in each residual block instead of only the second (ablation)Parallel adapters on ResNet-26 · Visual Decathlonmean top-1 accuracy: 77.40 -> 78.07Helped
jointly SVD-compress adapters across domains (ablation)Parallel adapters on ResNet-26 · Visual Decathlonmean top-1 accuracy: 78.07 -> 78.36Helped
use parallel adapters rather than series adapterspretrained ResNet-26 · Visual DecathlonDecathlon score: 3159 -> 3412Helped
append series adapters after pretraining instead of including them during pretraining (ablation)Series adapters on ResNet · Visual Decathlon, four datasetsmean top-1 accuracy: -1.73 pointHurt
apply dropout with a wider pretrained network (ablation)Parallel adapters · CIFAR100accuracyHelped
increase the number of pretrained ImageNet classes (ablation)ResNet with parallel adapters · CIFAR100accuracyHelped
use adapters instead of full fine-tuningResNet transfer learning · CIFAR100 and UCF101accuracyHelped

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