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In this paper, we propose BNAS-v2 to further improve the efficiency of NAS,embodying both superiorities of BCNN simultaneously. To mitigate the unfairtraining issue of BNAS, we employ continuous relaxation strategy to make eachedge of cell in BCNN relevant to all candidate operations forover-parameterized BCNN construction. Moreover, the continuous relaxationstrategy relaxes the choice of a candidate operation as a softmax over allpredefined operations. Consequently, BNAS-v2 employs the gradient-basedoptimization algorithm to simultaneously update every possible path ofover-parameterized BCNN, rather than the single sampled one as BNAS. However,continuous relaxation leads to another issue named performance collapse, inwhich those weight-free operations are prone to be selected by the searchstrategy. For this consequent issue, two solutions are given: 1) we proposeConfident Learning Rate (CLR) that considers the confidence of gradient forarchitecture weights update, increasing with the training time ofover-parameterized BCNN; 2) we introduce the combination of partial channelconnections and edge normalization that also can improve the memory efficiencyfurther. Moreover, we denote differentiable BNAS (i.e. BNAS with continuousrelaxation) as BNAS-D, BNAS-D with CLR as BNAS-v2-CLR, and partial-connectedBNAS-D as BNAS-v2-PC. Experimental results on CIFAR-10 and ImageNet show that1) BNAS-v2 delivers state-of-the-art search efficiency on both CIFAR-10 (0.05GPU days that is 4x faster than BNAS) and ImageNet (0.19 GPU days); and 2) theproposed CLR is effective to alleviate the performance collapse issue in bothBNAS-D and vanilla differentiable NAS framework.

BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search