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Deep Learning without Weight Transport

NeurIPS 2019 · Architectures and efficiency › RNNs and normalization · approach: Backprop-free credit assignment · 70 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use weight mirroring to learn feedback weights without weight transportResNet-18 · ImageNettop-1 test error: 97.4(2)% -> 30.2(7)%Helped
Use reciprocal Kolen-Pollack learning to learn feedback weightsResNet-18 · ImageNettop-1 test error: 97.4(2)% -> 29.2(4)%Helped
Use weight mirroring to learn feedback weights without weight transportResNet-50 · ImageNettop-1 test error: 98.9(1)% -> 23.4(5)%Helped
Use reciprocal Kolen-Pollack learning to learn feedback weightsResNet-50 · ImageNettop-1 test error: 98.9(1)% -> 23.9(7)%Helped
Apply BatchNorm before ReLU rather than after itSign-symmetry · ImageNet, ResNet-18top-1 test error: 39.2(4)% -> 37.8(4)%Helped
Apply BatchNorm before ReLU rather than after itSign-symmetry · ImageNet, ResNet-50top-1 test error: 33.8(3)% -> 32.6(6)%Helped
Apply BatchNorm before ReLU rather than after itBackprop, feedback alignment, Kolen-Pollack, and weight mirror · ImageNettop-1 test errorNo clear effect

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