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Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching

AAAI 2021 · Label-efficient and robust learning › Knowledge distillation · approach: Rethinking distillation objectives · 153 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use attention-based feature links instead of manually selected linksfeature distillation · tinyImageNet, ResNet34 → ResNet18accuracy: 0.6819 -> 0.6880Helped
Use attention-based feature links instead of manually selected linksfeature distillation · ImageNet, ResNet34 → ResNet18accuracy: 0.7137 -> 0.7138Helped
Use attention-based feature links instead of L2T linkstransfer learning · CUB200, MIT67, Stanford40accuracy: 0.6505 -> 0.6829; 0.6485 -> 0.6647; 0.6308 -> 0.6792Helped
Use attention-based feature links instead of L2T linkstransfer learning · Stanford Dogsaccuracy: 0.7808 -> 0.7606Hurt
Use AFD links instead of ordered manually selected links (ablation)ResNet34 → WRN-28-2 with equal-interval teacher candidates · CIFAR-100accuracy: 0.7544 -> 0.7723Helped
Co-train AFD links rather than use ordered links (ablation)ResNet34 → WRN-28-2 with equal-interval teacher candidates · CIFAR-100accuracy: 0.7544 -> 0.7726Helped
Use all teacher candidates when student candidates are numerous (ablation)AFD, ResNet56 → ResNet20 · CIFAR-100, 9 student candidatesaccuracy: 0.7115 -> 0.7153Helped
Use all teacher candidates with only 3 student candidates (ablation)AFD, ResNet56 → ResNet20 · CIFAR-100, 3 student candidatesaccuracy: 0.7140 -> 0.7109Hurt
Replace L2 distance with L1 distance (ablation)AFD · CIFAR-100, WRN-40-2 → WRN-16-2accuracy: 0.7547 -> 0.7513Hurt
Replace L2 distance with KL divergence (ablation)AFD · CIFAR-100, WRN-40-2 → WRN-16-2accuracy: 0.7547 -> 0.7523Hurt
Replace L2 distance with cosine similarity (ablation)AFD · CIFAR-100, WRN-40-2 → WRN-16-2accuracy: 0.7547 -> 0.7541Hurt
Replace A2 pooling with A1 average pooling (ablation)AFD · CIFAR-100, WRN-40-2 → WRN-16-2accuracy: 0.7547 -> 0.7520Hurt

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