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Theory-Inspired Path-Regularized Differential Network Architecture Search

NeurIPS 2020 · Architectures and efficiency › Neural architecture search · approach: Differentiable architecture search (DARTS) · 24 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace DARTS softmax operation weighting with group-structured sparse binary gates plus path-depth-wise regularization (PR-DARTS)DARTS (second-order) · CIFAR10 testtest error (%): 2.76 -> 2.32Helped
replace DARTS with PR-DARTSDARTS (second-order) · CIFAR100 testtest error (%): 17.54 -> 16.45Helped
replace DARTS with PR-DARTSDARTS (second-order) · ImageNet top-1top-1 error (%): 26.7 -> 24.1Helped
replace DARTS with PR-DARTS (random two-op update, sparsity pruning)DARTS (second-order) · CIFAR10 searchsearch cost (GPU-days): 4.0 -> 0.17Helped
add group-structured sparse binary gates (alone) to DARTS (ablation)PR-DARTS · CIFAR10 search, selected cell compositionskip-connection dominanceHelped
add path-depth-wise regularization to the gated search (ablation)PR-DARTS · CIFAR10 search, selected cell depthcell-selection bias to shallow cellsHelped
vary regularization weights lambda1 in [1e-2,1], lambda2 in [10^-4.5,10^-2.5], lambda3 in [1e-4,10^-1.5] (ablation)PR-DARTS · CIFAR10test error (%)No clear effect

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