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Learning Student-Friendly Teacher Networks for Knowledge Distillation

NeurIPS 2021 · Label-efficient and robust learning › Knowledge distillation · approach: Rethinking distillation objectives · 45 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
train teacher with student branches (SFTN) instead of standard teacherKD · CIFAR-100 resnet32x4->resnet8x4student accuracy: 73.39 -> 76.09Helped
train teacher with student branches (SFTN)KD · CIFAR-100 resnet32x4->ShuffleV1student accuracy: 74.26 -> 77.93Helped
train teacher with student branches (SFTN)KD · ImageNet ResNet50->ResNet34 top-1student accuracy: 73.55 -> 74.14Helped
SFTN teacher vs collaborative learning DMLKD · CIFAR-100 resnet32x4->ShuffleV1student accuracy: 73.37 -> 77.93Helped
SFTN teacher vs curriculum RCOKD · CIFAR-100 resnet32x4->ShuffleV1student accuracy: 76.62 -> 77.93Helped
train SFTN four times longer (SFTN-4) (ablation)SFTN · CIFAR-100 resnet32x4->ShuffleV1student accuracy: 77.93 -> 79.11Helped
increase KL weight lambda_KL from 1 to 3 (ablation)SFTN · CIFAR-100 KD average student accuracyaccuracy: 76.07 -> 76.71Helped
increase KL weight lambda_KL to 10 (ablation)SFTN · CIFAR-100 KD average student accuracyaccuracy: 76.71 -> 74.87Hurt
vary KL temperature tau~ from 1 to 20 (ablation)SFTN · CIFAR-100 student accuracyaccuracy: 75.58 -> 74.95No clear effect
fine-tune pretrained teacher with student branches instead of training from scratch (ablation)SFTN · CIFAR-100 resnet32x4->ShuffleV2average student accuracy: 78.81 -> 78.71No clear effect
use student branch with smaller capacity than student (ablation)SFTN · CIFAR-100 WRN40-2/WRN16-2average student accuracy: 76.66 -> 74.91Hurt
SFTN teacher vs standard teacher on corrupted dataKD · CIFAR-100-C intensity 5accuracy: 32.97 -> 33.15Helped

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