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Meta Convolutional Neural Networks for Single Domain Generalization

CVPR 2022 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Augmentation and mixture-of-experts generalization · 55 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace the baseline with MetaCNN using bag-of-meta-feature compositionDigits models trained on MNIST · Digits target Avgaccuracy: 74.46 -> 78.76Helped
replace the baseline with MetaCNN using bag-of-meta-feature compositionWideResNet models trained on CIFAR-10 · CIFAR-10-C severity 5 Avgaccuracy: 72.61 -> 78.45Helped
replace the baseline with MetaCNN using bag-of-meta-feature compositionAlexNet models trained on PACS photo · PACS target Avgaccuracy: 55.24 -> 57.17Helped
add local feature addressing to select related meta features (ablation)MetaCNN · Digits target Avgaccuracy: 74.28 -> 78.76Helped
increase the number of BoMF blocks from one to two (ablation)MetaCNN · Digits target Avgaccuracy: 77.21 -> 78.76Helped
increase the number of BoMF blocks from two to three (ablation)MetaCNN · Digits target Avgaccuracy: 78.76 -> 79.34Helped
increase the number of BoMF blocks from three to four (ablation)MetaCNN · Digits target Avgaccuracy: 79.34 -> 79.24Hurt
add BoMF compositionAlexNet trained on PACS photo · PACS art paintingaccuracy: 56.26 -> 54.05Hurt
add BoMF compositionAlexNet trained on PACS photo · PACS cartoon and sketchaccuracy: 51.04 -> 53.58; 58.42 -> 63.88Helped

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