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We develop an approach to efficiently grow neural networks, within whichparameterization and optimization strategies are designed by considering theireffects on the training dynamics. Unlike existing growing methods, which followsimple replication heuristics or utilize auxiliary gradient-based localoptimization, we craft a parameterization scheme which dynamically stabilizesweight, activation, and gradient scaling as the architecture evolves, andmaintains the inference functionality of the network. To address theoptimization difficulty resulting from imbalanced training effort distributedto subnetworks fading in at different growth phases, we propose a learning rateadaption mechanism that rebalances the gradient contribution of these separatesubcomponents. Experimental results show that our method achieves comparable orbetter accuracy than training large fixed-size models, while saving asubstantial portion of the original computation budget for training. Wedemonstrate that these gains translate into real wall-clock training speedups.

Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation