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Born Again Neural Networks

ICML 2018 · Label-efficient and robust learning › Knowledge distillation · approach: Distillation with causal and adaptive guidance · 431 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Distill from an identical DenseNet teacherDenseNet BAN · CIFAR-100test error: 17.69 -> 16.69Helped
Distill from an identical Wide-ResNet teacherWide-ResNet BAN · CIFAR-100test error: 30.05 -> 29.43; 25.32 -> 24.38; 20.88 -> 20.93; 19.08 -> 18.25Mixed
Train sequential BAN generationsDenseNet-80-80 · CIFAR-100test error: 17.16 -> 16.36; 15.5Helped
Ensemble multiple BAN generationsBAN DenseNet · CIFAR-100test error: 14.9Helped
Add ground-truth label loss to BAN distillation (ablation)DenseNet-90-60 BAN · CIFAR-100test error: 16.69 -> 16.93Hurt
Replace the teacher distribution with teacher-max-weighted labels (ablation)DenseNet BAN · CIFAR-100test error: 18.25 -> 17.84; 17.69 -> 17.42; 17.16 -> 17.16; 16.87 -> 17.12Mixed
Permute the teacher's non-argmax outputs (ablation)DenseNet BAN · CIFAR-100test error: 18.25 -> 17.84; 17.69 -> 17.43; 17.16 -> 16.84; 16.87 -> 16.34Helped
Reduce student depth to half the teacher depth (ablation)DenseNet-90-60 BAN · CIFAR-100test error: 17.69 -> 16.95Helped
Increase student depth to twice the teacher depth (ablation)DenseNet-90-60 BAN · CIFAR-100test error: 17.69 -> 16.43Helped
Halve the compression rate (ablation)DenseNet-90-60 BAN · CIFAR-100test error: 17.69 -> 19.83Hurt
Distill DenseNet-90-60 into a ResNet studentBAN-ResNet · CIFAR-100test error: 20.28 -> 18.8; 18.84 -> 17.39; 20.4 -> 19.12; 18.83 -> 17.42; 19.63 -> 17.13; 18.77 -> 17.18Helped
Distill with teacher outputs and label lossBAN language models · PTB testperplexity: 80.05 -> 76.97; 71.87 -> 68.56Helped

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