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In this paper, we propose a Collaboration of Experts (CoE) framework to pooltogether the expertise of multiple networks towards a common aim. Each expertis an individual network with expertise on a unique portion of the dataset,which enhances the collective capacity. Given a sample, an expert is selectedby the delegator, which simultaneously outputs a rough prediction to supportearly termination. To fulfill this framework, we propose three modules to impeleach model to play its role, namely weight generation module (WGM), labelgeneration module (LGM) and variance calculation module (VCM). Our methodachieves the state-of-the-art performance on ImageNet, 80.7% top-1 accuracywith 194M FLOPs. Combined with PWLU activation function and CondConv, CoEfurther achieves the accuracy of 80.0% with only 100M FLOPs for the first time.More importantly, our method is hardware friendly and achieves a 3-6x speedupcompared with some existing conditional computation approaches.

Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs