PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks

23 Jul 2020Ting-Wu ChinAri S. MorcosDiana Marculescu

Slimmable neural networks provide a flexible trade-off front between prediction error and computational cost (such as the number of floating-point operations or FLOPs) with the same storage cost as a single model, have been proposed recently for resource-constrained settings such as mobile devices. However, current slimmable neural networks use a single width-multiplier for all the layers to arrive at sub-networks with different performance profiles, which neglects that different layers affect the network's prediction accuracy differently and have different FLOP requirements... (read more)

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